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Learning When and Where to Zoom With Deep Reinforcement Learning | Burak Uzkent, Stefano Ermon | While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e.g. remote sensing, they can be much more expensive to acquire. For these reasons, it is desirable to develop an automatic method to selectively use high resolution data when necessary while maintaining accuracy and reducing acquisition/run-time cost. In this direction, we propose PatchDrop a reinforcement learning approach to dynamically identify when and where to use/acquire high resolution data conditioned on the paired, cheap, low resolution images. We conduct experiments on CIFAR10, CIFAR100, ImageNet and fMoW datasets where we use significantly less high resolution data while maintaining similar accuracy to models which use full high resolution images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Uzkent_Learning_When_and_Where_to_Zoom_With_Deep_Reinforcement_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00425 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Uzkent_Learning_When_and_Where_to_Zoom_With_Deep_Reinforcement_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Uzkent_Learning_When_and_Where_to_Zoom_With_Deep_Reinforcement_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Densely Connected Search Space for More Flexible Neural Architecture Search | Jiemin Fang, Yuzhu Sun, Qian Zhang, Yuan Li, Wenyu Liu, Xinggang Wang | Neural architecture search (NAS) has dramatically advanced the development of neural network design. We revisit the search space design in most previous NAS methods and find the number and widths of blocks are set manually. However, block counts and block widths determine the network scale (depth and width) and make a great influence on both the accuracy and the model cost (FLOPs/latency). In this paper, we propose to search block counts and block widths by designing a densely connected search space, i.e., DenseNAS. The new search space is represented as a dense super network, which is built upon our designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. We further propose a chained cost estimation algorithm to approximate the model cost during the search. Both the accuracy and model cost are optimized in DenseNAS. For experiments on the MobileNetV2-based search space, DenseNAS achieves 75.3% top-1 accuracy on ImageNet with only 361MB FLOPs and 17.9ms latency on a single TITAN-XP. The larger model searched by DenseNAS achieves 76.1% accuracy with only 479M FLOPs. DenseNAS further promotes the ImageNet classification accuracies of ResNet-18, -34 and -50-B by 1.5%, 0.5% and 0.3% with 200M, 600M and 680M FLOPs reduction respectively. The related code is available at https://github.com/JaminFong/DenseNAS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fang_Densely_Connected_Search_Space_for_More_Flexible_Neural_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.09607 | https://www.youtube.com/watch?v=fK6PfOVLLnM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Densely_Connected_Search_Space_for_More_Flexible_Neural_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Densely_Connected_Search_Space_for_More_Flexible_Neural_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fang_Densely_Connected_Search_CVPR_2020_supplemental.pdf | null | null |
Neural Topological SLAM for Visual Navigation | Devendra Singh Chaplot, Ruslan Salakhutdinov, Abhinav Gupta, Saurabh Gupta | This paper studies the problem of image-goal navigation which involves navigating to the location indicated by a goal image in a novel previously unseen environment. To tackle this problem, we design topological representations for space that effectively leverage semantics and afford approximate geometric reasoning. At the heart of our representations are nodes with associated semantic features, that are interconnected using coarse geometric information. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation. Experimental study in visually and physically realistic simulation suggests that our method builds effective representations that capture structural regularities and efficiently solve long-horizon navigation problems. We observe a relative improvement of more than 50% over existing methods that study this task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chaplot_Neural_Topological_SLAM_for_Visual_Navigation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.12256 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chaplot_Neural_Topological_SLAM_for_Visual_Navigation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chaplot_Neural_Topological_SLAM_for_Visual_Navigation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chaplot_Neural_Topological_SLAM_CVPR_2020_supplemental.pdf | null | null |
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data | Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira | Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN, proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hsu_Generalized_ODIN_Detecting_Out-of-Distribution_Image_Without_Learning_From_Out-of-Distribution_Data_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11297 | https://www.youtube.com/watch?v=fwQO0Lpd65Y | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hsu_Generalized_ODIN_Detecting_Out-of-Distribution_Image_Without_Learning_From_Out-of-Distribution_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hsu_Generalized_ODIN_Detecting_Out-of-Distribution_Image_Without_Learning_From_Out-of-Distribution_Data_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hsu_Generalized_ODIN_Detecting_CVPR_2020_supplemental.pdf | null | null |
Sequential Motif Profiles and Topological Plots for Offline Signature Verification | Elias N. Zois, Evangelos Zervas, Dimitrios Tsourounis, George Economou | In spite of the overwhelming high-tech marvels and applications that rule our digital lives, the use of the handwritten signature is still recognized worldwide in government, personal and legal entities to be the most important behavioral biometric trait. A number of notable research approaches provide advanced results up to a certain point which allow us to assert with confidence that the performance attained by signature verification (SV) systems is comparable to those provided by any other biometric modality. Up to now, the mainstream trend for offline SV is shared between standard -or handcrafted- feature extraction methods and popular machine learning techniques, with typical examples ranging from sparse representation to Deep Learning. Recent progress in graph mining algorithms provide us with the prospect to re-evaluate the opportunity of utilizing graph representations by exploring corresponding graph features for offline SV. In this paper, inspired by the recent use of image visibility graphs for mapping images into networks, we introduce for the first time in offline SV literature their use as a parameter free, agnostic representation for exploring global as well as local information. Global properties of the sparsely located content of the shape of the signature image are encoded with topological information of the whole graph. In addition, local pixel patches are encoded by sequential visibility motifs-subgraphs of size four, to a low six dimensional motif profile vector. A number of pooling functions operate on the motif codes in a spatial pyramid context in order to create the final feature vector. The effectiveness of the proposed method is evaluated with the use of two popular datasets. The local visibility graph features are considered to be highly informative for SV; this is sustained by the corresponding results which are at least comparable with other classic state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zois_Sequential_Motif_Profiles_and_Topological_Plots_for_Offline_Signature_Verification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ZLOb8qwB54U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zois_Sequential_Motif_Profiles_and_Topological_Plots_for_Offline_Signature_Verification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zois_Sequential_Motif_Profiles_and_Topological_Plots_for_Offline_Signature_Verification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes | Mahyar Najibi, Guangda Lai, Abhijit Kundu, Zhichao Lu, Vivek Rathod, Thomas Funkhouser, Caroline Pantofaru, David Ross, Larry S. Davis, Alireza Fathi | We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. 3D bounding box parameters are estimated in one pass for every point, aggregated through graph convolutions, and fed into a branch of the network that predicts latent codes representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-to-end training of the 3D object detection pipeline. Thus our model is able to extract shapes without access to ground-truth shape information in the target dataset. During experiments, we find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Waymo Open Dataset, while reproducing the shapes of detected cars. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Najibi_DOPS_Learning_to_Detect_3D_Objects_and_Predict_Their_3D_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01170 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Najibi_DOPS_Learning_to_Detect_3D_Objects_and_Predict_Their_3D_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Najibi_DOPS_Learning_to_Detect_3D_Objects_and_Predict_Their_3D_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multimodal Categorization of Crisis Events in Social Media | Mahdi Abavisani, Liwei Wu, Shengli Hu, Joel Tetreault, Alejandro Jaimes | Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time. Emergency response is one such area that stands to gain from these advances. By processing billions of texts and images a minute, events can be automatically detected to enable emergency response workers to better assess rapidly evolving situations and deploy resources accordingly. To date, most event detection techniques in this area have focused on image-only or text-only approaches, limiting detection performance and impacting the quality of information delivered to crisis response teams. In this paper, we present a new multimodal fusion method that leverages both images and texts as input. In particular, we introduce a cross-attention module that can filter uninformative and misleading components from weak modalities on a sample by sample basis. In addition, we employ a multimodal graph-based approach to stochastically transition between embeddings of different multimodal pairs during training to better regularize the learning process as well as dealing with limited training data by constructing new matched pairs from different samples. We show that our method outperforms the unimodal approaches and strong multimodal baselines by a large margin on three crisis-related tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Abavisani_Multimodal_Categorization_of_Crisis_Events_in_Social_Media_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04917 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Abavisani_Multimodal_Categorization_of_Crisis_Events_in_Social_Media_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Abavisani_Multimodal_Categorization_of_Crisis_Events_in_Social_Media_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Abavisani_Multimodal_Categorization_of_CVPR_2020_supplemental.pdf | null | null |
Physically Realizable Adversarial Examples for LiDAR Object Detection | James Tu, Mengye Ren, Sivabalan Manivasagam, Ming Liang, Bin Yang, Richard Du, Frank Cheng, Raquel Urtasun | Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks have only been applied to 2D flat images. In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. We report attack results on a suite of detectors using various input representation of point clouds. We also conduct a pilot study on adversarial defense using data augmentation. This is one step closer towards safer self-driving under unseen conditions from limited training data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tu_Physically_Realizable_Adversarial_Examples_for_LiDAR_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00543 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tu_Physically_Realizable_Adversarial_Examples_for_LiDAR_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tu_Physically_Realizable_Adversarial_Examples_for_LiDAR_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tu_Physically_Realizable_Adversarial_CVPR_2020_supplemental.zip | null | null |
STEFANN: Scene Text Editor Using Font Adaptive Neural Network | Prasun Roy, Saumik Bhattacharya, Subhankar Ghosh, Umapada Pal | Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/1903.01192 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Roy_STEFANN_Scene_Text_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/360 | null |
SwapText: Image Based Texts Transfer in Scenes | Qiangpeng Yang, Jun Huang, Wei Lin | Swapping text in scene images while preserving original fonts, colors, sizes and background textures is a challenging task due to the complex interplay between different factors. In this work, we present SwapText, a three-stage framework to transfer texts across scene images. First, a novel text swapping network is proposed to replace text labels only in the foreground image. Second, a background completion network is learned to reconstruct background images. Finally, the generated foreground image and background image are used to generate the word image by the fusion network. Using the proposing framework, we can manipulate the texts of the input images even with severe geometric distortion. Qualitative and quantitative results are presented on several scene text datasets, including regular and irregular text datasets. We conducted extensive experiments to prove the usefulness of our method such as image based text translation, text image synthesis. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_SwapText_Image_Based_Texts_Transfer_in_Scenes_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08152 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_SwapText_Image_Based_Texts_Transfer_in_Scenes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_SwapText_Image_Based_Texts_Transfer_in_Scenes_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation | Rongchang Xie, Chunyu Wang, Yizhou Wang | Cross view feature fusion is the key to address the occlusion problem in human pose estimation. The current fusion methods need to train a separate model for every pair of cameras making them difficult to scale. In this work, we introduce MetaFuse, a pre-trained fusion model learned from a large number of cameras in the Panoptic dataset. The model can be efficiently adapted or finetuned for a new pair of cameras using a small number of labeled images. The strong adaptation power of MetaFuse is due in large part to the proposed factorization of the original fusion model into two parts--(1) a generic fusion model shared by all cameras, and (2) lightweight camera-dependent transformations. Furthermore, the generic model is learned from many cameras by a meta-learning style algorithm to maximize its adaptation capability to various camera poses. We observe in experiments that MetaFuse finetuned on the public datasets outperforms the state-of-the-arts by a large margin which validates its value in practice. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_MetaFuse_A_Pre-trained_Fusion_Model_for_Human_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13239 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_MetaFuse_A_Pre-trained_Fusion_Model_for_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_MetaFuse_A_Pre-trained_Fusion_Model_for_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xie_MetaFuse_A_Pre-trained_CVPR_2020_supplemental.pdf | null | null |
Local-Global Video-Text Interactions for Temporal Grounding | Jonghwan Mun, Minsu Cho, Bohyung Han | This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query. We tackle this problem using a novel regression-based model that learns to extract a collection of mid-level features for semantic phrases in a text query, which corresponds to important semantic entities described in the query (e.g., actors, objects, and actions), and reflect bi-modal interactions between the linguistic features of the query and the visual features of the video in multiple levels. The proposed method effectively predicts the target time interval by exploiting contextual information from local to global during bi-modal interactions. Through in-depth ablation studies, we find out that incorporating both local and global context in video and text interactions is crucial to the accurate grounding. Our experiment shows that the proposed method outperforms the state of the arts on Charades-STA and ActivityNet Captions datasets by large margins, 7.44% and 4.61% points at Recall@tIoU=0.5 metric, respectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mun_Local-Global_Video-Text_Interactions_for_Temporal_Grounding_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07514 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mun_Local-Global_Video-Text_Interactions_for_Temporal_Grounding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mun_Local-Global_Video-Text_Interactions_for_Temporal_Grounding_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mun_Local-Global_Video-Text_Interactions_CVPR_2020_supplemental.pdf | null | null |
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps | Pengxiang Wu, Siheng Chen, Dimitris N. Metaxas | The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we propose an efficient deep model, called MotionNet, to jointly perform perception and motion prediction from 3D point clouds. MotionNet takes a sequence of LiDAR sweeps as input and outputs a bird's eye view (BEV) map, which encodes the object category and motion information in each grid cell. The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses. Extensive experiments show that the proposed method overall outperforms the state-of-the-arts, including the latest scene-flow- and 3D-object-detection-based methods. This indicates the potential value of the proposed method serving as a backup to the bounding-box-based system, and providing complementary information to the motion planner in autonomous driving. Code is available at https://www.merl.com/research/license#MotionNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_MotionNet_Joint_Perception_and_Motion_Prediction_for_Autonomous_Driving_Based_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=YodXMWLDKKo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_MotionNet_Joint_Perception_and_Motion_Prediction_for_Autonomous_Driving_Based_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_MotionNet_Joint_Perception_and_Motion_Prediction_for_Autonomous_Driving_Based_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Improving Action Segmentation via Graph-Based Temporal Reasoning | Yifei Huang, Yusuke Sugano, Yoichi Sato | Temporal relations among multiple action segments play an important role in action segmentation especially when observations are limited (e.g., actions are occluded by other objects or happen outside a field of view). In this paper, we propose a network module called Graph-based Temporal Reasoning Module (GTRM) that can be built on top of existing action segmentation models to learn the relation of multiple action segments in various time spans. We model the relations by using two Graph Convolution Networks (GCNs) where each node represents an action segment. The two graphs have different edge properties to account for boundary regression and classification tasks, respectively. By applying graph convolution, we can update each node's representation based on its relation with neighboring nodes. The updated representation is then used for improved action segmentation. We evaluate our model on the challenging egocentric datasets namely EGTEA and EPIC-Kitchens, where actions may be partially observed due to the viewpoint restriction. The results show that our proposed GTRM outperforms state-of-the-art action segmentation models by a large margin. We also demonstrate the effectiveness of our model on two third-person video datasets, the 50Salads dataset and the Breakfast dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Improving_Action_Segmentation_via_Graph-Based_Temporal_Reasoning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Improving_Action_Segmentation_via_Graph-Based_Temporal_Reasoning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Improving_Action_Segmentation_via_Graph-Based_Temporal_Reasoning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huang_Improving_Action_Segmentation_CVPR_2020_supplemental.pdf | null | null |
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers | Chi Zhang, Yujun Cai, Guosheng Lin, Chunhua Shen | In this paper, we address the few-shot classification task from a new perspective of optimal matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance. The EMD generates the optimal matching flows between structural elements that have the minimum matching cost, which is used to represent the image distance for classification. To generate the important weights of elements in the EMD formulation, we design a cross-reference mechanism, which can effectively minimize the impact caused by the cluttered background and large intra-class appearance variations. To handle k-shot classification, we propose to learn a structured fully connected layer that can directly classify dense image representations with the EMD. Based on the implicit function theorem, the EMD can be inserted as a layer into the network for end-to-end training. We conduct comprehensive experiments to validate our algorithm and we set new state-of-the-art performance on four popular few-shot classification benchmarks, namely miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100) and Caltech-UCSD Birds-200-2011 (CUB). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_DeepEMD_Few-Shot_Image_Classification_With_Differentiable_Earth_Movers_Distance_and_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=X2ZUZYy_GtY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_DeepEMD_Few-Shot_Image_Classification_With_Differentiable_Earth_Movers_Distance_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_DeepEMD_Few-Shot_Image_Classification_With_Differentiable_Earth_Movers_Distance_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Conditional Gaussian Distribution Learning for Open Set Recognition | Xin Sun, Zhenning Yang, Chi Zhang, Keck-Voon Ling, Guohao Peng | Deep neural networks have achieved state-of-the-art performance in a wide range of recognition/classification tasks. However, when applying deep learning to real-world applications, there are still multiple challenges. A typical challenge is that unknown samples may be fed into the system during the testing phase and traditional deep neural networks will wrongly recognize the unknown sample as one of the known classes. Open set recognition is a potential solution to overcome this problem, where the open set classifier should have the ability to reject unknown samples as well as maintain high classification accuracy on known classes. The variational auto-encoder (VAE) is a popular model to detect unknowns, but it cannot provide discriminative representations for known classification. In this paper, we propose a novel method, Conditional Gaussian Distribution Learning (CGDL), for open set recognition. In addition to detecting unknown samples, this method can also classify known samples by forcing different latent features to approximate different Gaussian models. Meanwhile, to avoid information hidden in the input vanishing in the middle layers, we also adopt the probabilistic ladder architecture to extract high-level abstract features. Experiments on several standard image datasets reveal that the proposed method significantly outperforms the baseline method and achieves new state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Conditional_Gaussian_Distribution_Learning_for_Open_Set_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08823 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Conditional_Gaussian_Distribution_Learning_for_Open_Set_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Conditional_Gaussian_Distribution_Learning_for_Open_Set_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
D2Det: Towards High Quality Object Detection and Instance Segmentation | Jiale Cao, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao | We propose a novel two-stage detection method, D2Det, that collectively addresses both precise localization and accurate classification. For precise localization, we introduce a dense local regression that predicts multiple dense box offsets for an object proposal. Different from traditional regression and keypoint-based localization employed in two-stage detectors, our dense local regression is not limited to a quantized set of keypoints within a fixed region and has the ability to regress position-sensitive real number dense offsets, leading to more precise localization. The dense local regression is further improved by a binary overlap prediction strategy that reduces the influence of background region on the final box regression. For accurate classification, we introduce a discriminative RoI pooling scheme that samples from various sub-regions of a proposal and performs adaptive weighting to obtain discriminative features. On MS COCO test-dev, our D2Det outperforms existing two-stage methods, with a single-model performance of 45.4 AP, using ResNet101 backbone. When using multi-scale training and inference, D2Det obtains AP of 50.1. In addition to detection, we adapt D2Det for instance segmentation, achieving a mask AP of 40.2 with a two-fold speedup, compared to the state-of-the-art. We also demonstrate the effectiveness of our D2Det on airborne sensors by performing experiments for object detection in UAV images (UAVDT dataset) and instance segmentation in satellite images (iSAID dataset). Source code is available at https://github.com/JialeCao001/D2Det. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Rethinking Classification and Localization for Object Detection | Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu | Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Rethinking_Classification_and_Localization_for_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.06493 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Rethinking_Classification_and_Localization_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Rethinking_Classification_and_Localization_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer | Jan Svoboda, Asha Anoosheh, Christian Osendorfer, Jonathan Masci | This paper introduces a neural style transfer model to generate a stylized image conditioning on a set of examples describing the desired style. The proposed solution produces high-quality images even in the zero-shot setting and allows for more freedom in changes to the content geometry. This is made possible by introducing a novel Two-Stage Peer-Regularization Layer that recombines style and content in latent space by means of a custom graph convolutional layer. Contrary to the vast majority of existing solutions, our model does not depend on any pre-trained networks for computing perceptual losses and can be trained fully end-to-end thanks to a new set of cyclic losses that operate directly in latent space and not on the RGB images. An extensive ablation study confirms the usefulness of the proposed losses and of the Two-Stage Peer-Regularization Layer, with qualitative results that are competitive with respect to the current state of the art using a single model for all presented styles. This opens the door to more abstract and artistic neural image generation scenarios, along with simpler deployment of the model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Svoboda_Two-Stage_Peer-Regularized_Feature_Recombination_for_Arbitrary_Image_Style_Transfer_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.02913 | https://www.youtube.com/watch?v=s4EV7RJ8svg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Svoboda_Two-Stage_Peer-Regularized_Feature_Recombination_for_Arbitrary_Image_Style_Transfer_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Svoboda_Two-Stage_Peer-Regularized_Feature_Recombination_for_Arbitrary_Image_Style_Transfer_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Svoboda_Two-Stage_Peer-Regularized_Feature_CVPR_2020_supplemental.pdf | null | null |
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data | Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, Dimitris N. Metaxas | In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic image solely to train the segmentation model. We validate the proposed framework on the application of health entities learning problem which is known to be privacy sensitive. Our experiments show that our approach: 1) could learn the real image's distribution from multiple datasets without sharing the patient's raw data. 2) is more efficient and requires lower bandwidth than other distributed deep learning methods. 3) achieves higher performance compared to the model trained by one real dataset, and almost the same performance compared to the model trained by all real datasets. 4) has provable guarantees that the generator could learn the distributed distribution in an all important fashion thus is unbiased.We release our AsynDGAN source code at: https://github.com/tommy-qichang/AsynDGAN | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chang_Synthetic_Learning_Learn_From_Distributed_Asynchronized_Discriminator_GAN_Without_Sharing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.00080 | https://www.youtube.com/watch?v=04lUG4iMZ6Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Synthetic_Learning_Learn_From_Distributed_Asynchronized_Discriminator_GAN_Without_Sharing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Synthetic_Learning_Learn_From_Distributed_Asynchronized_Discriminator_GAN_Without_Sharing_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chang_Synthetic_Learning_Learn_CVPR_2020_supplemental.pdf | null | null |
BEDSR-Net: A Deep Shadow Removal Network From a Single Document Image | Yun-Hsuan Lin, Wen-Chin Chen, Yung-Yu Chuang | Removing shadows in document images enhances both the visual quality and readability of digital copies of documents. Most existing shadow removal algorithms for document images use hand-crafted heuristics and are often not robust to documents with different characteristics. This paper proposes the Background Estimation Document Shadow Removal Network (BEDSR-Net), the first deep network specifically designed for document image shadow removal. For taking advantage of specific properties of document images, a background estimation module is designed for extracting the global background color of the document. During the process of estimating the background color, the module also learns information about the spatial distribution of background and non-background pixels. We encode such information into an attention map. With the estimated global background color and attention map, the shadow removal network can better recover the shadow-free image. We also show that the model trained on synthetic images remains effective for real photos, and provide a large set of synthetic shadow images of documents along with their corresponding shadow-free images and shadow masks. Extensive quantitative and qualitative experiments on several benchmarks show that the BEDSR-Net outperforms existing methods in enhancing both the visual quality and readability of document images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_BEDSR-Net_A_Deep_Shadow_Removal_Network_From_a_Single_Document_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_BEDSR-Net_A_Deep_Shadow_Removal_Network_From_a_Single_Document_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_BEDSR-Net_A_Deep_Shadow_Removal_Network_From_a_Single_Document_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Label Decoupling Framework for Salient Object Detection | Jun Wei, Shuhui Wang, Zhe Wu, Chi Su, Qingming Huang, Qi Tian | To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxiliary supervision. Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it is to be predicted, because edge pixels have a very imbalance distribution. To address this problem, we propose a label decoupling framework (LDF) which consists of a label decoupling (LD) procedure and a feature interaction network (FIN). LD explicitly decomposes the original saliency map into body map and detail map, where body map concentrates on center areas of objects and detail map focuses on regions around edges. Detail map works better because it involves much more pixels than traditional edge supervision. Different from saliency map, body map discards edge pixels and only pays attention to center areas. This successfully avoids the distraction from edge pixels during training. Therefore, we employ two branches in FIN to deal with body map and detail map respectively. Feature interaction (FI) is designed to fuse the two complementary branches to predict the saliency map, which is then used to refine the two branches again. This iterative refinement is helpful for learning better representations and more precise saliency maps. Comprehensive experiments on six benchmark datasets demonstrate that LDF outperforms state-of-the-art approaches on different evaluation metrics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_Label_Decoupling_Framework_for_Salient_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2008.11048 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Label_Decoupling_Framework_for_Salient_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Label_Decoupling_Framework_for_Salient_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks | Hang Zhou, Dongdong Chen, Jing Liao, Kejiang Chen, Xiaoyi Dong, Kunlin Liu, Weiming Zhang, Gang Hua, Nenghai Yu | Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samples produced from various attack methods, including optimization-based 3D Carlini-Wagner attack, gradient-based iterative fast gradient method, and skeleton-detach based point-dropping. However, after a careful analysis, these methods are either extremely slow because of the optimization/iterative scheme, or not flexible to support targeted attack of a specific category. To overcome these shortcomings, this paper proposes a novel label guided adversarial network (LG-GAN) for real-time flexible targeted point cloud attack. To the best of our knowledge, this is the first generation based 3D point cloud attack method. By feeding the original point clouds and target attack label into LG-GAN, it can learn how to deform the point clouds to mislead the recognition network into the specific label only with a single forward pass. In detail, LG-GAN first leverages one multi-branch adversarial network to extract hierarchical features of the input point clouds, then incorporates the specified label information into multiple intermediate features using the label encoder. Finally, the encoded features will be fed into the coordinate reconstruction decoder to generate the target adversarial sample. By evaluating different point-cloud recognition models (e.g., PointNet, PointNet++ and DGCNN), we demonstrate that the proposed LG-GAN can support flexible targeted attack on the fly while guaranteeing good attack performance and higher efficiency simultaneously. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_LG-GAN_Label_Guided_Adversarial_Network_for_Flexible_Targeted_Attack_of_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Se9IbnH6e7c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_LG-GAN_Label_Guided_Adversarial_Network_for_Flexible_Targeted_Attack_of_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_LG-GAN_Label_Guided_Adversarial_Network_for_Flexible_Targeted_Attack_of_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_LG-GAN_Label_Guided_CVPR_2020_supplemental.pdf | null | null |
Look-Into-Object: Self-Supervised Structure Modeling for Object Recognition | Mohan Zhou, Yalong Bai, Wei Zhang, Tiejun Zhao, Tao Mei | Most object recognition approaches predominantly focus on learning discriminative visual patterns, while overlooking the holistic object structure. Though important, structure modeling usually requires significant manual annotations and therefore is labor-intensive. In this paper, we propose to "look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions into the traditional framework. We show the recognition backbone can be substantially enhanced for more robust representation learning, without any cost of extra annotation and inference speed. Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category. We then design a spatial context learning module for modeling the internal structures of the object, through predicting the relative positions within the extent. These two modules can be easily plugged into any backbone networks during training and detached at inference time. Extensive experiments show that our look-into-object approach (LIO) achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft). We also show that this learning paradigm is highly generalizable to other tasks such as object detection and segmentation (MS COCO). Project page: https://github.com/JDAI-CV/LIO. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Inferring Attention Shift Ranks of Objects for Image Saliency | Avishek Siris, Jianbo Jiao, Gary K.L. Tam, Xianghua Xie, Rynson W.H. Lau | Psychology studies and behavioural observation show that humans shift their attention from one location to another when viewing an image of a complex scene. This is due to the limited capacity of the human visual system in simultaneously processing multiple visual inputs. The sequential shifting of attention on objects in a non-task oriented viewing can be seen as a form of saliency ranking. Although there are methods proposed for predicting saliency rank, they are not able to model this human attention shift well, as they are primarily based on ranking saliency values from binary prediction. Following psychological studies, in this paper, we propose to predict the saliency rank by inferring human attention shift. Due to the lack of such data, we first construct a large-scale salient object ranking dataset. The saliency rank of objects is defined by the order that an observer attends to these objects based on attention shift. The final saliency rank is an average across the saliency ranks of multiple observers. We then propose a learning-based CNN to leverage both bottom-up and top-down attention mechanisms to predict the saliency rank. Experimental results show that the proposed network achieves state-of-the-art performances on salient object rank prediction. Code and dataset are available at https://github.com/SirisAvishek/Attention_Shift_Ranks | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Siris_Inferring_Attention_Shift_Ranks_of_Objects_for_Image_Saliency_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Siris_Inferring_Attention_Shift_Ranks_of_Objects_for_Image_Saliency_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Siris_Inferring_Attention_Shift_Ranks_of_Objects_for_Image_Saliency_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Siris_Inferring_Attention_Shift_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/325 | null |
Music Gesture for Visual Sound Separation | Chuang Gan, Deng Huang, Hang Zhao, Joshua B. Tenenbaum, Antonio Torralba | Recent deep learning approaches have achieved impressive performance on visual sound separation tasks. However, these approaches are mostly built on appearance and optical flow like motion feature representations, which exhibit limited abilities to find the correlations between audio signals and visual points, especially when separating multiple instruments of the same types, such as multiple violins in a scene. To address this, we propose "Music Gesture," a keypoint-based structured representation to explicitly model the body and finger movements of musicians when they perform music. We first adopt a context-aware graph network to integrate visual semantic context with body dynamics and then apply an audio-visual fusion model to associate body movements with the corresponding audio signals. Experimental results on three music performance datasets show: 1) strong improvements upon benchmark metrics for hetero-musical separation tasks (i.e. different instruments); 2) new ability for effective homo-musical separation for piano, flute, and trumpet duets, which to our best knowledge has never been achieved with alternative methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gan_Music_Gesture_for_Visual_Sound_Separation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.09476 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gan_Music_Gesture_for_Visual_Sound_Separation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gan_Music_Gesture_for_Visual_Sound_Separation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Exploring Categorical Regularization for Domain Adaptive Object Detection | Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, Xiu-Shen Wei | In this paper, we tackle the domain adaptive object detection problem, where the main challenge lies in significant domain gaps between source and target domains. Previous work seeks to plainly align image-level and instance-level shifts to eventually minimize the domain discrepancy. However, they still overlook to match crucial image regions and important instances across domains, which will strongly affect domain shift mitigation. In this work, we propose a simple but effective categorical regularization framework for alleviating this issue. It can be applied as a plug-and-play component on a series of Domain Adaptive Faster R-CNN methods which are prominent for dealing with domain adaptive detection. Specifically, by integrating an image-level multi-label classifier upon the detection backbone, we can obtain the sparse but crucial image regions corresponding to categorical information, thanks to the weakly localization ability of the classification manner. Meanwhile, at the instance level, we leverage the categorical consistency between image-level predictions (by the classifier) and instance-level predictions (by the detection head) as a regularization factor to automatically hunt for the hard aligned instances of target domains. Extensive experiments of various domain shift scenarios show that our method obtains a significant performance gain over original Domain Adaptive Faster R-CNN detectors. Furthermore, qualitative visualization and analyses can demonstrate the ability of our method for attending on the key regions/instances targeting on domain adaptation. Our code is open-source and available at https://github.com/Megvii-Nanjing/CR-DA-DET. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Exploring_Categorical_Regularization_for_Domain_Adaptive_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09152 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Exploring_Categorical_Regularization_for_Domain_Adaptive_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Exploring_Categorical_Regularization_for_Domain_Adaptive_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_Exploring_Categorical_Regularization_CVPR_2020_supplemental.pdf | null | null |
Incremental Learning in Online Scenario | Jiangpeng He, Runyu Mao, Zeman Shao, Fengqing Zhu | Modern deep learning approaches have achieved great success in many vision applications by training a model using all available task-specific data. However, there are two major obstacles making it challenging to implement for real life applications: (1) Learning new classes makes the trained model quickly forget old classes knowledge, which is referred to as catastrophic forgetting. (2) As new observations of old classes come sequentially over time, the distribution may change in unforeseen way, making the performance degrade dramatically on future data, which is referred to as concept drift. Current state-of-the-art incremental learning methods require a long time to train the model whenever new classes are added and none of them takes into consideration the new observations of old classes. In this paper, we propose an incremental learning framework that can work in the challenging online learning scenario and handle both new classes data and new observations of old classes. We address problem (1) in online mode by introducing a modified cross-distillation loss together with a two-step learning technique. Our method outperforms the results obtained from current state-of-the-art offline incremental learning methods on the CIFAR-100 and ImageNet-1000 (ILSVRC 2012) datasets under the same experiment protocol but in online scenario. We also provide a simple yet effective method to mitigate problem (2) by updating exemplar set using the feature of each new observation of old classes and demonstrate a real life application of online food image classification based on our complete framework using the Food-101 dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/He_Incremental_Learning_in_Online_Scenario_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13191 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Incremental_Learning_in_Online_Scenario_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Incremental_Learning_in_Online_Scenario_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features | Xiang Li, Yasushi Makihara, Chi Xu, Yasushi Yagi, Mingwu Ren | Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e.g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations due to the covariate overwhelm those due to the identity. We therefore propose a method of gait recognition via disentangled representation learning that considers both identity and covariate features. Specifically, we first encode an input gait template to get the disentangled identity and covariate features, and then decode the features to simultaneously reconstruct the input gait template and the canonical version of the same subject with no covariates in a semi-supervised manner to ensure successful disentanglement. We finally feed the disentangled identity features into a contrastive/triplet loss function for a verification/identification task. Moreover, we find that new gait templates can be synthesized by transferring the covariate feature from one subject to another. Experimental results on three publicly available gait data sets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Gait_Recognition_via_Semi-supervised_Disentangled_Representation_Learning_to_Identity_and_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=aVrRZs91Jqc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Gait_Recognition_via_Semi-supervised_Disentangled_Representation_Learning_to_Identity_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Gait_Recognition_via_Semi-supervised_Disentangled_Representation_Learning_to_Identity_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Gait_Recognition_via_CVPR_2020_supplemental.zip | null | null |
Discovering Human Interactions With Novel Objects via Zero-Shot Learning | Suchen Wang, Kim-Hui Yap, Junsong Yuan, Yap-Peng Tan | We aim to detect human interactions with novel objects through zero-shot learning. Different from previous works, we allow unseen object categories by using its semantic word embedding. To do so, we design a human-object region proposal network specifically for the human-object interaction detection task. The core idea is to leverage human visual clues to localize objects which are interacting with humans. We show that our proposed model can outperform existing methods on detecting interacting objects, and generalize well to novel objects. To recognize objects from unseen categories, we devise a zero-shot classification module upon the classifier of seen categories. It utilizes the classifier logits for seen categories to estimate a vector in the semantic space, and then performs nearest search to find the closest unseen category. We validate our method on V-COCO and HICO-DET datasets, and obtain superior results on detecting human interactions with both seen and unseen objects. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Discovering_Human_Interactions_With_Novel_Objects_via_Zero-Shot_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Discovering_Human_Interactions_With_Novel_Objects_via_Zero-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Discovering_Human_Interactions_With_Novel_Objects_via_Zero-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Discovering_Human_Interactions_CVPR_2020_supplemental.pdf | null | null |
Visual Commonsense R-CNN | Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun | We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also the core reason why VC R-CNN can learn "sense-making" knowledge like chair can be sat -- while not just "common" co-occurrences such as chair is likely to exist if table is observed. We extensively apply VC R-CNN features in prevailing models of three popular tasks: Image Captioning, VQA, and VCR, and observe consistent performance boosts across them, achieving many new state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Visual_Commonsense_R-CNN_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Visual_Commonsense_R-CNN_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Visual_Commonsense_R-CNN_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Visual_Commonsense_R-CNN_CVPR_2020_supplemental.pdf | null | null |
Squeeze-and-Attention Networks for Semantic Segmentation | Zilong Zhong, Zhong Qiu Lin, Rene Bidart, Xiaodan Hu, Ibrahim Ben Daya, Zhifeng Li, Wei-Shi Zheng, Jonathan Li, Alexander Wong | The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features. However, these attention mechanisms ignore an implicit sub-task of semantic segmentation and are constrained by the grid structure of convolution kernels. In this paper, we propose a novel squeeze-and-attention network (SANet) architecture that leverages an effective squeeze-and-attention (SA) module to account for two distinctive characteristics of segmentation: i) pixel-group attention, and ii) pixel-wise prediction. Specifically, the proposed SA modules impose pixel-group attention on conventional convolution by introducing an 'attention' convolutional channel, thus taking into account spatial-channel inter-dependencies in an efficient manner. The final segmentation results are produced by merging outputs from four hierarchical stages of a SANet to integrate multi-scale contexts for obtaining an enhanced pixel-wise prediction. Empirical experiments on two challenging public datasets validate the effectiveness of the proposed SANets, which achieves 83.2 % mIoU (without COCO pre-training) on PASCAL VOC and a state-of-the-art mIoU of 54.4 % on PASCAL Context. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhong_Squeeze-and-Attention_Networks_for_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.03402 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Squeeze-and-Attention_Networks_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Squeeze-and-Attention_Networks_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
UNAS: Differentiable Architecture Search Meets Reinforcement Learning | Arash Vahdat, Arun Mallya, Ming-Yu Liu, Jan Kautz | Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning (RL) based NAS. However, DNAS models can only optimize differentiable loss functions in search, and they require an accurate differentiable approximation of non-differentiable criteria. In this work, we present UNAS, a unified framework for NAS, that encapsulates recent DNAS and RL-based approaches under one framework. Our framework brings the best of both worlds, and it enables us to search for architectures with both differentiable and non-differentiable criteria in one unified framework while maintaining a low search cost. Further, we introduce a new objective function for search based on the generalization gap that prevents the selection of architectures prone to overfitting. We present extensive experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets and we perform search in two fundamentally different search spaces. We show that UNAS obtains the state-of-the-art average accuracy on all three datasets when compared to the architectures searched in the DARTS space. Moreover, we show that UNAS can find an efficient and accurate architecture in the ProxylessNAS search space, that outperforms existing MobileNetV2 based architectures. The source code is available at https://github.com/NVlabs/unas. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Vahdat_UNAS_Differentiable_Architecture_Search_Meets_Reinforcement_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07651 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Vahdat_UNAS_Differentiable_Architecture_Search_Meets_Reinforcement_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Vahdat_UNAS_Differentiable_Architecture_Search_Meets_Reinforcement_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Vahdat_UNAS_Differentiable_Architecture_CVPR_2020_supplemental.pdf | null | null |
Multiple Anchor Learning for Visual Object Detection | Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, Dong Huang | Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate (or anchor) bounding boxes. This configuration significantly limits the possibility to jointly optimize classification and localization. In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector. Our approach, referred to as Multiple Anchor Learning (MAL), constructs anchor bags and selects the most representative anchors from each bag. Such an iterative selection process is potentially NP-hard to optimize. To address this issue, we solve MAL by repetitively depressing the confidence of selected anchors by perturbing their corresponding features. In an adversarial selection-depression manner, MAL not only pursues optimal solutions but also fully leverages multiple anchors/features to learn a detection model. Experiments show that MAL improves the baseline RetinaNet with significant margins on the commonly used MS-COCO object detection benchmark and achieves new state-of-the-art detection performance compared with recent methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ke_Multiple_Anchor_Learning_for_Visual_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02252 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ke_Multiple_Anchor_Learning_for_Visual_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ke_Multiple_Anchor_Learning_for_Visual_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Explaining Knowledge Distillation by Quantifying the Knowledge | Xu Cheng, Zhefan Rao, Yilan Chen, Quanshi Zhang | This paper presents a method to interpret the success of knowledge distillation by quantifying and analyzing task-relevant and task-irrelevant visual concepts that are encoded in intermediate layers of a deep neural network (DNN). More specifically, three hypotheses are proposed as follows. 1. Knowledge distillation makes the DNN learn more visual concepts than learning from raw data. 2. Knowledge distillation ensures that the DNN is prone to learning various visual concepts simultaneously. Whereas, in the scenario of learning from raw data, the DNN learns visual concepts sequentially. 3. Knowledge distillation yields more stable optimization directions than learning from raw data. Accordingly, we design three types of mathematical metrics to evaluate feature representations of the DNN. In experiments, we diagnosed various DNNs, and above hypotheses were verified. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cheng_Explaining_Knowledge_Distillation_by_Quantifying_the_Knowledge_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03622 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Explaining_Knowledge_Distillation_by_Quantifying_the_Knowledge_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Explaining_Knowledge_Distillation_by_Quantifying_the_Knowledge_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Unifying Training and Inference for Panoptic Segmentation | Qizhu Li, Xiaojuan Qi, Philip H.S. Torr | We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things". In contrast to recent works, our network exploits a parametrised, yet lightweight panoptic segmentation submodule, powered by an end-to-end learnt dense instance affinity, to capture the probability that any pair of pixels belong to the same instance. This panoptic submodule gives rise to a novel propagation mechanism for panoptic logits and enables the network to output a coherent panoptic segmentation map for both "stuff" and "thing" classes, without any post-processing. Reaping the benefits of end-to-end training, our full system sets new records on the popular street scene dataset, Cityscapes, achieving 61.4 PQ with a ResNet-50 backbone using only the fine annotations. On the challenging COCO dataset, our ResNet-50-based network also delivers state-of-the-art accuracy of 43.4 PQ. Moreover, our network flexibly works with and without object mask cues, performing competitively under both settings, which is of interest for applications with computation budgets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Unifying_Training_and_Inference_for_Panoptic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.04982 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Unifying_Training_and_Inference_for_Panoptic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Unifying_Training_and_Inference_for_Panoptic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Unifying_Training_and_CVPR_2020_supplemental.pdf | null | null |
Scalable Uncertainty for Computer Vision With Functional Variational Inference | Eduardo D. C. Carvalho, Ronald Clark, Andrea Nicastro, Paul H. J. Kelly | As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world. In this work, we leverage the formulation of variational inference in function space, where we associate Gaussian Processes (GPs) to both Bayesian CNN priors and variational family. Since GPs are fully determined by their mean and covariance functions, we are able to obtain predictive uncertainty estimates at the cost of a single forward pass through any chosen CNN architecture and for any supervised learning task. By leveraging the structure of the induced covariance matrices, we propose numerically efficient algorithms which enable fast training in the context of high-dimensional tasks such as depth estimation and semantic segmentation. Additionally, we provide sufficient conditions for constructing regression loss functions whose probabilistic counterparts are compatible with aleatoric uncertainty quantification. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Carvalho_Scalable_Uncertainty_for_Computer_Vision_With_Functional_Variational_Inference_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03396 | https://www.youtube.com/watch?v=lFYmP9mkWTY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Carvalho_Scalable_Uncertainty_for_Computer_Vision_With_Functional_Variational_Inference_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Carvalho_Scalable_Uncertainty_for_Computer_Vision_With_Functional_Variational_Inference_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Carvalho_Scalable_Uncertainty_for_CVPR_2020_supplemental.pdf | null | null |
X-Linear Attention Networks for Image Captioning | Yingwei Pan, Ting Yao, Yehao Li, Tao Mei | Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2nd order interactions across multi-modal inputs. Nevertheless, there has not been evidence in support of building such interactions concurrently with attention mechanism for image captioning. In this paper, we introduce a unified attention block --- X-Linear attention block, that fully employs bilinear pooling to selectively capitalize on visual information or perform multi-modal reasoning. Technically, X-Linear attention block simultaneously exploits both the spatial and channel-wise bilinear attention distributions to capture the 2^ nd order interactions between the input single-modal or multi-modal features. Higher and even infinity order feature interactions are readily modeled through stacking multiple X-Linear attention blocks and equipping the block with Exponential Linear Unit (ELU) in a parameter-free fashion, respectively. Furthermore, we present X-Linear Attention Networks (dubbed as X-LAN) that novelly integrates X-Linear attention block(s) into image encoder and sentence decoder of image captioning model to leverage higher order intra- and inter-modal interactions. The experiments on COCO benchmark demonstrate that our X-LAN obtains to-date the best published CIDEr performance of 132.0% on COCO Karpathy test split. When further endowing Transformer with X-Linear attention blocks, CIDEr is boosted up to 132.8%. Source code is available at https://github.com/Panda-Peter/image-captioning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_X-Linear_Attention_Networks_for_Image_Captioning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_X-Linear_Attention_Networks_for_Image_Captioning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_X-Linear_Attention_Networks_for_Image_Captioning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions | Evonne Ng, Donglai Xiang, Hanbyul Joo, Kristen Grauman | The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera. We propose a learning-based approach to estimate the camera wearer's 3D body pose from egocentric video sequences. Our key insight is to leverage interactions with another person---whose body pose we can directly observe---as a signal inherently linked to the body pose of the first-person subject. We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses, it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view. We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation, which improves the state of the art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ng_You2Me_Inferring_Body_Pose_in_Egocentric_Video_via_First_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.09882 | https://www.youtube.com/watch?v=BUObbn7i_qo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ng_You2Me_Inferring_Body_Pose_in_Egocentric_Video_via_First_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ng_You2Me_Inferring_Body_Pose_in_Egocentric_Video_via_First_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Estimating Low-Rank Region Likelihood Maps | Gabriela Csurka, Zoltan Kato, Andor Juhasz, Martin Humenberger | Low-rank regions capture geometrically meaningful structures in an image which encompass typical local features such as edges, corners and all kinds of regular, symmetric, often repetitive patterns, that are commonly found in man-made environment. While such patterns are challenging current state-of-the-art feature correspondence methods, the recovered homography of a low-rank texture readily provides 3D structure with respect to a 3D plane, without any prior knowledge of the visual information on that plane. However, the automatic and efficient detection of the broad class of low-rank regions is unsolved. Herein, we propose a novel self-supervised low-rank region detection deep network that predicts a low-rank likelihood map from an image. The evaluation of our method on real-world datasets shows not only that it reliably predicts low-rank regions in the image similarly to our baseline method, but thanks to the data augmentations used in the training phase it generalizes well to difficult cases (e.g. day/night lighting, low contrast, underexposure) where the baseline prediction fails. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Csurka_Estimating_Low-Rank_Region_Likelihood_Maps_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Csurka_Estimating_Low-Rank_Region_Likelihood_Maps_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Csurka_Estimating_Low-Rank_Region_Likelihood_Maps_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network | Yuliang Liu, Hao Chen, Chunhua Shen, Tong He, Lianwen Jin, Liangwei Wang | Scene text detection and recognition has received increasing research attention. Existing methods can be roughly categorized into two groups: character-based and segmentation-based. These methods either are costly for character annotation or need to maintain a complex pipeline, which is often not suitable for real-time applications. Here we address the problem by proposing the Adaptive Bezier-Curve Network (\BeCan). Our contributions are three-fold: 1) For the first time, we adaptively fit oriented or curved text by a parameterized Bezier curve. 2) We design a novel BezierAlign layer for extracting accurate convolution features of a text instance with arbitrary shapes, significantly improving the precision compared with previous methods. 3) Compared with standard bounding box detection, our Bezier curve detection introduces negligible computation overhead, resulting in superiority of our method in both efficiency and accuracy. Experiments on oriented or curved benchmark datasets, namely Total-Text and CTW1500, demonstrate that \BeCan achieves state-of-the-art accuracy, meanwhile significantly improving the speed. In particular, on Total-Text, our real-time version is over 10 times faster than recent state-of-the-art methods with a competitive recognition accuracy. Code is available at https://git.io/AdelaiDet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_ABCNet_Real-Time_Scene_Text_Spotting_With_Adaptive_Bezier-Curve_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10200 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_ABCNet_Real-Time_Scene_Text_Spotting_With_Adaptive_Bezier-Curve_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_ABCNet_Real-Time_Scene_Text_Spotting_With_Adaptive_Bezier-Curve_Network_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation | Shurui Gui, Chaoyue Wang, Qihua Chen, Dacheng Tao | Video interpolation aims to synthesize non-existent frames between two consecutive frames. Although existing optical flow based methods have achieved promising results, they still face great challenges in dealing with the interpolation of complicated dynamic scenes, which include occlusion, blur or abrupt brightness change. This is mainly because these cases may break the basic assumptions of the optical flow estimation (i.e. smoothness, consistency). In this work, we devised a novel structure-to-texture generation framework which splits the video interpolation task into two stages: structure-guided interpolation and texture refinement. In the first stage, deep structure-aware features are employed to predict feature flows from two consecutive frames to their intermediate result, and further generate the structure image of the intermediate frame. In the second stage, based on the generated coarse result, a Frame Texture Compensator is trained to fill in detailed textures. To the best of our knowledge, this is the first work that attempts to directly generate the intermediate frame through blending deep features. Experiments on both the benchmark datasets and challenging occlusion cases demonstrate the superiority of the proposed framework over the state-of-the-art methods. Codes are available on https://github.com/CM-BF/FeatureFlow. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=M4YIVzayO3Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gui_FeatureFlow_Robust_Video_CVPR_2020_supplemental.pdf | null | null |
Attention-Guided Hierarchical Structure Aggregation for Image Matting | Yu Qiao, Yuhao Liu, Xin Yang, Dongsheng Zhou, Mingliang Xu, Qiang Zhang, Xiaopeng Wei | Existing deep learning based matting algorithms primarily resort to high-level semantic features to improve the overall structure of alpha mattes. However, we argue that advanced semantics extracted from CNNs contribute unequally for alpha perception and we are supposed to reconcile advanced semantic information with low-level appearance cues to refine the foreground details. In this paper, we propose an end-to-end Hierarchical Attention Matting Network (HAttMatting), which can predict the better structure of alpha mattes from single RGB images without additional input. Specifically, we employ spatial and channel-wise attention to integrate appearance cues and pyramidal features in a novel fashion. This blended attention mechanism can perceive alpha mattes from refined boundaries and adaptive semantics. We also introduce a hybrid loss function fusing Structural SIMilarity (SSIM), Mean Square Error (MSE) and Adversarial loss to guide the network to further improve the overall foreground structure. Besides, we construct a large-scale image matting dataset comprised of 59,600 training images and 1000 test images (total 646 distinct foreground alpha mattes), which can further improve the robustness of our hierarchical structure aggregation model. Extensive experiments demonstrate that the proposed HAttMatting can capture sophisticated foreground structure and achieve state-of-the-art performance with single RGB images as input. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qiao_Attention-Guided_Hierarchical_Structure_Aggregation_for_Image_Matting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_Attention-Guided_Hierarchical_Structure_Aggregation_for_Image_Matting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_Attention-Guided_Hierarchical_Structure_Aggregation_for_Image_Matting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qiao_Attention-Guided_Hierarchical_Structure_CVPR_2020_supplemental.pdf | null | null |
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation | Koutilya PNVR, Hao Zhou, David Jacobs | We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.04026 | https://www.youtube.com/watch?v=BsCGfVKhZOs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/PNVR_SharinGAN_Combining_Synthetic_CVPR_2020_supplemental.pdf | null | null |
Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels | Junran Peng, Xingyuan Bu, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan | Training with more data has always been the most stable and effective way of improving performance in deep learn-ing era. As the largest object detection dataset so far, OpenImages brings great opportunities and challenges for object detection in general and sophisticated scenarios. However, owing to its semi-automatic collecting and labeling pipeline to deal with the huge data scale, Open Images dataset suffers from label-related problems that objects may explicitly or implicitly have multiple labels and the label distribution is extremely imbalanced. In this work, we quantitatively analyze these label problems and provide a simple but effective solution. We design a concurrent softmax to handle the multi-label problems in object detection and propose a soft-sampling methods with hybrid training scheduler to deal with the label imbalance. Overall, our method yields a dramatic improvement of 3.34 points, leading to the best single model with 60.90 mAP on the public object detection test set of Open Images. And our ensembling result achieves 67.17mAP, which is 4.29 points higher than the first place method last year. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Peng_Large-Scale_Object_Detection_in_the_Wild_From_Imbalanced_Multi-Labels_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.08455 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Peng_Large-Scale_Object_Detection_in_the_Wild_From_Imbalanced_Multi-Labels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Peng_Large-Scale_Object_Detection_in_the_Wild_From_Imbalanced_Multi-Labels_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Peng_Large-Scale_Object_Detection_CVPR_2020_supplemental.pdf | null | null |
AugFPN: Improving Multi-Scale Feature Learning for Object Detection | Chaoxu Guo, Bin Fan, Qian Zhang, Shiming Xiang, Chunhong Pan | Current state-of-the-art detectors typically exploit feature pyramid to detect objects at different scales. Among them, FPN is one of the representative works that build a feature pyramid by multi-scale features summation. However, the design defects behind prevent the multi-scale features from being fully exploited. In this paper, we begin by first analyzing the design defects of feature pyramid in FPN, and then introduce a new feature pyramid architecture named AugFPN to address these problems. Specifically, AugFPN consists of three components: Consistent Supervision, Residual Feature Augmentation, and Soft RoI Selection. AugFPN narrows the semantic gaps between features of different scales before feature fusion through Consistent Supervision. In feature fusion, ratio-invariant context information is extracted by Residual Feature Augmentation to reduce the information loss of feature map at the highest pyramid level. Finally, Soft RoI Selection is employed to learn a better RoI feature adaptively after feature fusion. By replacing FPN with AugFPN in Faster R-CNN, our models achieve 2.3 and 1.6 points higher Average Precision (AP) when using ResNet50 and MobileNet-v2 as backbone respectively. Furthermore, AugFPN improves RetinaNet by 1.6 points AP and FCOS by 0.9 points AP when using ResNet50 as backbone. Codes are available on https://github.com/Gus-Guo/AugFPN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_AugFPN_Improving_Multi-Scale_Feature_Learning_for_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.05384 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_AugFPN_Improving_Multi-Scale_Feature_Learning_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_AugFPN_Improving_Multi-Scale_Feature_Learning_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Residual Flow for Out of Distribution Detection | Ev Zisselman, Aviv Tamar | The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of feature activations in the training data for adequately distinguishing abnormalities, and the state-of-the-art method uses Gaussian distribution models. In this work, we present a novel approach that improves upon the state-of-the-art by leveraging an expressive density model based on normalizing flows. We introduce the residual flow, a novel flow architecture that learns the residual distribution from a base Gaussian distribution. Our model is general, and can be applied to any data that is approximately Gaussian. For out of distribution detection in image datasets, our approach provides a principled improvement over the state-of-the-art. Specifically, we demonstrate the effectiveness of our method in ResNet and DenseNet architectures trained on various image datasets. For example, on a ResNet trained on CIFAR-100 and evaluated on detection of out-of-distribution samples from the ImageNet dataset, holding the true positive rate (TPR) at 95%, we improve the true negative rate (TNR) from 56.7% (current state of-the-art) to 77.5% (ours). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zisselman_Deep_Residual_Flow_for_Out_of_Distribution_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.05419 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zisselman_Deep_Residual_Flow_for_Out_of_Distribution_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zisselman_Deep_Residual_Flow_for_Out_of_Distribution_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zisselman_Deep_Residual_Flow_CVPR_2020_supplemental.pdf | null | null |
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias | Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, Deepti Ghadiyaram | Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model's generalizability, especially when typical co-occurrence patterns are absent. This work focuses on addressing such contextual biases to improve the robustness of the learnt feature representations. Our goal is to accurately recognize a category in the absence of its context, without compromising on performance when it co-occurs with context. Our key idea is to decorrelate feature representations of a category from its co-occurring context. We achieve this by learning a feature subspace that explicitly represents categories occurring in the absence of context along side a joint feature subspace that represents both categories and context. Our very simple yet effective method is extensible to two multi-label tasks -- object and attribute classification. On 4 challenging datasets, we demonstrate the effectiveness of our method in reducing contextual bias. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Singh_Dont_Judge_an_Object_by_Its_Context_Learning_to_Overcome_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Singh_Dont_Judge_an_Object_by_Its_Context_Learning_to_Overcome_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Singh_Dont_Judge_an_Object_by_Its_Context_Learning_to_Overcome_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Singh_Dont_Judge_an_CVPR_2020_supplemental.pdf | null | null |
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition | Zhi Qiao, Yu Zhou, Dongbao Yang, Yucan Zhou, Weiping Wang | Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur, uneven illumination, and incomplete characters. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we propose a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts. The semantic information is used both in the encoder module for supervision and in the decoder module for initializing. In particular, the state-of-the-art ASTER method is integrated into the proposed framework as an exemplar. Extensive experiments demonstrate that the proposed framework is more robust for low-quality text images, and achieves state-of-the-art results on several benchmark datasets. The source code will be available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qiao_SEED_Semantics_Enhanced_Encoder-Decoder_Framework_for_Scene_Text_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.10977 | https://www.youtube.com/watch?v=2I-bY8XpoGc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_SEED_Semantics_Enhanced_Encoder-Decoder_Framework_for_Scene_Text_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_SEED_Semantics_Enhanced_Encoder-Decoder_Framework_for_Scene_Text_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Iterative Answer Prediction With Pointer-Augmented Multimodal Transformers for TextVQA | Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach | Many visual scenes contain text that carries crucial information, and it is thus essential to understand text in images for downstream reasoning tasks. For example, a deep water label on a warning sign warns people about the danger in the scene. Recent work has explored the TextVQA task that requires reading and understanding text in images to answer a question. However, existing approaches for TextVQA are mostly based on custom pairwise fusion mechanisms between a pair of two modalities and are restricted to a single prediction step by casting TextVQA as a classification task. In this work, we propose a novel model for the TextVQA task based on a multimodal transformer architecture accompanied by a rich representation for text in images. Our model naturally fuses different modalities homogeneously by embedding them into a common semantic space where self-attention is applied to model inter- and intra- modality context. Furthermore, it enables iterative answer decoding with a dynamic pointer network, allowing the model to form an answer through multi-step prediction instead of one-step classification. Our model outperforms existing approaches on three benchmark datasets for the TextVQA task by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Iterative_Answer_Prediction_With_Pointer-Augmented_Multimodal_Transformers_for_TextVQA_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.06258 | https://www.youtube.com/watch?v=DodK0kGCjME | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Iterative_Answer_Prediction_With_Pointer-Augmented_Multimodal_Transformers_for_TextVQA_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Iterative_Answer_Prediction_With_Pointer-Augmented_Multimodal_Transformers_for_TextVQA_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hu_Iterative_Answer_Prediction_CVPR_2020_supplemental.pdf | null | null |
iTAML: An Incremental Task-Agnostic Meta-learning Approach | Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah | Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new task. In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks. In this pursuit, we introduce a novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks. This is ensured by a new meta-update rule which avoids catastrophic forgetting. In comparison to previous meta-learning techniques, our approach is task-agnostic. When presented with a continuum of data, our model automatically identifies the task and quickly adapts to it with just a single update. We perform extensive experiments on five datasets in a class-incremental setting, leading to significant improvements over the state of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks). Specifically, on large-scale datasets that generally prove difficult cases for incremental learning, our approach delivers absolute gains as high as 19.1% and 7.4% on ImageNet and MS-Celeb datasets, respectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rajasegaran_iTAML_An_Incremental_Task-Agnostic_Meta-learning_Approach_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11652 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rajasegaran_iTAML_An_Incremental_Task-Agnostic_Meta-learning_Approach_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rajasegaran_iTAML_An_Incremental_Task-Agnostic_Meta-learning_Approach_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rajasegaran_iTAML_An_Incremental_CVPR_2020_supplemental.pdf | null | null |
Open Compound Domain Adaptation | Ziwei Liu, Zhongqi Miao, Xingang Pan, Xiaohang Zhan, Dahua Lin, Stella X. Yu, Boqing Gong | A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing works always assume that there exist clear distinctions between the domains, which is often not true in practice (e.g., changes in weather). We study an open compound domain adaptation (OCDA) problem, in which the target is a compound of multiple homogeneous domains without domain labels, reflecting realistic data collection from mixed and novel situations. We propose a new approach based on two technical insights into OCDA: 1) a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and 2) a memory module to increase the model's agility towards novel domains. Our experiments on digit classification, facial expression recognition, semantic segmentation, and reinforcement learning demonstrate the effectiveness of our approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Open_Compound_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.03403 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Open_Compound_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Open_Compound_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction | Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson | Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both 4x and 8x acceleration. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sriram_GrappaNet_Combining_Parallel_Imaging_With_Deep_Learning_for_Multi-Coil_MRI_CVPR_2020_paper.pdf | http://arxiv.org/abs/1910.12325 | https://www.youtube.com/watch?v=x07xGPg8-1g | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sriram_GrappaNet_Combining_Parallel_Imaging_With_Deep_Learning_for_Multi-Coil_MRI_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sriram_GrappaNet_Combining_Parallel_Imaging_With_Deep_Learning_for_Multi-Coil_MRI_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sriram_GrappaNet_Combining_Parallel_CVPR_2020_supplemental.pdf | null | null |
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions | Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, Peter Vajda, Joseph E. Gonzalez | Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's search space is small when compared to other search methods', since all candidate network layers must be explicitly instantiated in memory. To address this bottleneck, we propose a memory and computationally efficient DNAS variant: DMaskingNAS. This algorithm expands the search space by up to 10^14x over conventional DNAS, supporting searches over spatial and channel dimensions that are otherwise prohibitively expensive: input resolution and number of filters. We propose a masking mechanism for feature map reuse, so that memory and computational costs stay nearly constant as the search space expands. Furthermore, we employ effective shape propagation to maximize per-FLOP or per-parameter accuracy. The searched FBNetV2s yield state-of-the-art performance when compared with all previous architectures. With up to 421x less search cost, DMaskingNAS finds models with 0.9% higher accuracy, 15% fewer FLOPs than MobileNetV3-Small; and with similar accuracy but 20% fewer FLOPs than Efficient-B0. Furthermore, our FBNetV2 outperforms MobileNetV3 by 2.6% in accuracy, with equivalent model size. FBNetV2 models are open-sourced at https://github.com/facebookresearch/mobile-vision. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wan_FBNetV2_Differentiable_Neural_Architecture_Search_for_Spatial_and_Channel_Dimensions_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05565 | https://www.youtube.com/watch?v=sghzV7KFQX8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_FBNetV2_Differentiable_Neural_Architecture_Search_for_Spatial_and_Channel_Dimensions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_FBNetV2_Differentiable_Neural_Architecture_Search_for_Spatial_and_Channel_Dimensions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wan_FBNetV2_Differentiable_Neural_CVPR_2020_supplemental.pdf | null | null |
Webly Supervised Knowledge Embedding Model for Visual Reasoning | Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang | Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-available web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Webly_Supervised_Knowledge_Embedding_Model_for_Visual_Reasoning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Webly_Supervised_Knowledge_Embedding_Model_for_Visual_Reasoning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Webly_Supervised_Knowledge_Embedding_Model_for_Visual_Reasoning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Scale-Equalizing Pyramid Convolution for Object Detection | Xinjiang Wang, Shilong Zhang, Zhuoran Yu, Litong Feng, Wayne Zhang | Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different levels while seldom touching the inter-level correlation in the feature pyramid. Early computer vision methods extracted scale-invariant features by locating the feature extrema in both spatial and scale dimension. Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and spatial) features and outperforms other meticulously designed feature fusion modules. Based on the viewpoint of 3-D convolution, an integrated batch normalization that collects statistics from the whole feature pyramid is naturally inserted after the pyramid convolution. Furthermore, we also show that the naive pyramid convolution, together with the design of RetinaNet head, actually best applies for extracting features from a Gaussian pyramid, whose properties can hardly be satisfied by a feature pyramid. In order to alleviate this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that aligns the shared pyramid convolution kernel only at high-level feature maps. Being computationally efficient and compatible with the head design of most single-stage object detectors, the SEPC module brings significant performance improvement (>4AP increase on MS-COCO2017 dataset) in state-of-the-art one-stage object detectors, and a light version of SEPC also has 3.5AP gain with only around 7% inference time increase. The pyramid convolution also functions well as a stand-alone module in two-stage object detectors and is able to improve the performance by 2AP. The source code can be found at https://github.com/jshilong/SEPC. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Scale-Equalizing_Pyramid_Convolution_for_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03101 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Scale-Equalizing_Pyramid_Convolution_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Scale-Equalizing_Pyramid_Convolution_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Scale-Equalizing_Pyramid_Convolution_CVPR_2020_supplemental.pdf | null | null |
Learning Selective Self-Mutual Attention for RGB-D Saliency Detection | Nian Liu, Ni Zhang, Junwei Han | Saliency detection on RGB-D images is receiving more and more research interests recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input RGB and depth data or their saliency maps, which incur the problem of distribution gap or information loss. Some other models use the feature fusion scheme but are limited by the linear feature fusion methods. In this paper, we propose to fuse attention learned in both modalities. Inspired by the Non-local model, we integrate the self-attention and each other's attention to propagate long-range contextual dependencies, thus incorporating multi-modal information to learn attention and propagate contexts more accurately. Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term. We embed the proposed attention module in a two-stream CNN for RGB-D saliency detection. Furthermore, we also propose a residual fusion module to fuse the depth decoder features into the RGB stream. Experimental results on seven benchmark datasets demonstrate the effectiveness of the proposed model components and our final saliency model. Our code and saliency maps are available at https://github.com/nnizhang/S2MA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Prior Guided GAN Based Semantic Inpainting | Avisek Lahiri, Arnav Kumar Jain, Sanskar Agrawal, Pabitra Mitra, Prabir Kumar Biswas | Contemporary deep learning based semantic inpainting can be approached from two directions. First, and the more explored, approach is to train an offline deep regression network over the masked pixels with an additional refinement by adversarial training. This approach requires a single feed-forward pass for inpainting at inference. Another promising, yet unexplored approach is to first train a generative model to map a latent prior distribution to natural image manifold and during inference time search for the best-matching prior to reconstruct the signal. The primary aversion towards the latter genre is due to its inference time iterative optimization and difficulty to scale to higher resolution. In this paper, going against the general trend, we focus on the second paradigm of inpainting and address both of its mentioned problems. Most importantly, we learn a data driven parametric network to directly predict a matching prior for a given masked image. This converts an iterative paradigm to a single feed forward inference pipeline with around 800X speedup. We also regularize our network with structural prior (computed from the masked image itself) which helps in better preservation of pose and size of the object to be inpainted. Moreover, to extend our model for sequence reconstruction, we propose a recurrent net based grouped latent prior learning. Finally, we leverage recent advancements in high resolution GAN training to scale our inpainting network to 256X256. Experiments (spanning across resolutions from 64X64 to 256X256) conducted on SVHN, Standford Cars, CelebA, CelebA-HQ and ImageNet image datasets, and FaceForensics video datasets reveal that we consistently improve upon contemporary benchmarks from both schools of approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lahiri_Prior_Guided_GAN_Based_Semantic_Inpainting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lahiri_Prior_Guided_GAN_Based_Semantic_Inpainting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lahiri_Prior_Guided_GAN_Based_Semantic_Inpainting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning | Jaime Spencer, Richard Bowden, Simon Hadfield | In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency. Such approaches lack robustness and are unable to generalize to challenging domains such as nighttime scenes or adverse weather conditions where assumptions about photometric consistency break down. We propose DeFeat-Net (Depth & Feature network), an approach to simultaneously learn a cross-domain dense feature representation, alongside a robust depth-estimation framework based on warped feature consistency. The resulting feature representation is learned in an unsupervised manner with no explicit ground-truth correspondences required. We show that within a single domain, our technique is comparable to both the current state of the art in monocular depth estimation and supervised feature representation learning. However, by simultaneously learning features, depth and motion, our technique is able to generalize to challenging domains, allowing DeFeat-Net to outperform the current state-of-the-art with around 10% reduction in all error measures on more challenging sequences such as nighttime driving. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Spencer_DeFeat-Net_General_Monocular_Depth_via_Simultaneous_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=VR_nd9HKeqc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Spencer_DeFeat-Net_General_Monocular_Depth_via_Simultaneous_Unsupervised_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Spencer_DeFeat-Net_General_Monocular_Depth_via_Simultaneous_Unsupervised_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Regularizing Class-Wise Predictions via Self-Knowledge Distillation | Sukmin Yun, Jongjin Park, Kimin Lee, Jinwoo Shin | Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. In particular, we distill the predictive distribution between different samples of the same label during training. This results in regularizing the dark knowledge (i.e., the knowledge on wrong predictions) of a single network (i.e., a self-knowledge distillation) by forcing it to produce more meaningful and consistent predictions in a class-wise manner. Consequently, it mitigates overconfident predictions and reduces intra-class variations. Our experimental results on various image classification tasks demonstrate that the simple yet powerful method can significantly improve not only the generalization ability but also the calibration performance of modern convolutional neural networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yun_Regularizing_Class-Wise_Predictions_via_Self-Knowledge_Distillation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13964 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yun_Regularizing_Class-Wise_Predictions_via_Self-Knowledge_Distillation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yun_Regularizing_Class-Wise_Predictions_via_Self-Knowledge_Distillation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yun_Regularizing_Class-Wise_Predictions_CVPR_2020_supplemental.pdf | null | null |
Modality Shifting Attention Network for Multi-Modal Video Question Answering | Junyeong Kim, Minuk Ma, Trung Pham, Kyungsu Kim, Chang D. Yoo | This paper considers a network referred to as Modality Shifting Attention Network (MSAN) for Multimodal Video Question Answering (MVQA) task. MSAN decomposes the task into two sub-tasks: (1) localization of temporal moment relevant to the question, and (2) accurate prediction of the answer based on the localized moment. The modality required for temporal localization may be different from that for answer prediction, and this ability to shift modality is essential for performing the task. To this end, MSAN is based on (1) the moment proposal network (MPN) that attempts to locate the most appropriate temporal moment from each of the modalities, and also on (2) the heterogeneous reasoning network (HRN) that predicts the answer using an attention mechanism on both modalities. MSAN is able to place importance weight on the two modalities for each sub-task using a component referred to as Modality Importance Modulation (MIM). Experimental results show that MSAN outperforms previous state-of-the-art by achieving 71.13% test accuracy on TVQA benchmark dataset. Extensive ablation studies and qualitative analysis are conducted to validate various components of the network. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Modality_Shifting_Attention_Network_for_Multi-Modal_Video_Question_Answering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.02036 | https://www.youtube.com/watch?v=bEWFEB3yNFU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Modality_Shifting_Attention_Network_for_Multi-Modal_Video_Question_Answering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Modality_Shifting_Attention_Network_for_Multi-Modal_Video_Question_Answering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Vision-Language Navigation With Self-Supervised Auxiliary Reasoning Tasks | Fengda Zhu, Yi Zhu, Xiaojun Chang, Xiaodan Liang | Vision-Language Navigation (VLN) is a task where an agent learns to navigate following a natural language instruction. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches fully exploit vision and language features in cross-modal grounding. However, the VLN task remains challenging, since previous works have implicitly neglected the rich semantic information contained in environments (such as navigation graphs or sub-trajectory semantics). In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to exploit the additional training signals derived from these semantic information. The auxiliary tasks have four reasoning objectives: explaining the previous actions, evaluating the trajectory consistency, estimating the progress and predict the next direction. As a result, these additional training signals help the agent to acquire knowledge of semantic representations in order to reason about its activities and build a thorough perception of environments. Our experiments demonstrate that auxiliary reasoning tasks improve both the performance of the main task and the model generalizability by a large margin. We further demonstrate empirically that an agent trained with self-supervised auxiliary reasoning tasks substantially outperforms the previous state-of-the-art method, being the best existing approach on the standard benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Vision-Language_Navigation_With_Self-Supervised_Auxiliary_Reasoning_Tasks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07883 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Vision-Language_Navigation_With_Self-Supervised_Auxiliary_Reasoning_Tasks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Vision-Language_Navigation_With_Self-Supervised_Auxiliary_Reasoning_Tasks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hypergraph Attention Networks for Multimodal Learning | Eun-Sol Kim, Woo Young Kang, Kyoung-Woon On, Yu-Jung Heo, Byoung-Tak Zhang | One of the fundamental problems that arise in multimodal learning tasks is the disparity of information levels between different modalities. To resolve this problem, we propose Hypergraph Attention Networks (HANs), which define a common semantic space among the modalities with symbolic graphs and extract a joint representation of the modalities based on a co-attention map constructed in the semantic space. HANs follow the process: constructing the common semantic space with symbolic graphs of each modality, matching the semantics between sub-structures of the symbolic graphs, constructing co-attention maps between the graphs in the semantic space, and integrating the multimodal inputs using the co-attention maps to get the final joint representation. From the qualitative analysis with two Visual Question and Answering datasets, we discover that 1) the alignment of the information levels between the modalities is important, and 2) the symbolic graphs are very powerful ways to represent the information of the low-level signals in alignment. Moreover, HANs dramatically improve the state-of-the-art accuracy on the GQA dataset from 54.6% to 61.88% only using the symbolic information in quantitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Hypergraph_Attention_Networks_for_Multimodal_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Hypergraph_Attention_Networks_for_Multimodal_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Hypergraph_Attention_Networks_for_Multimodal_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real | Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, Mohi Khansari | Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manually engineering or prior learning a perception system. However, data for RL is collected via running an agent in the desired environment, and for applications like robotics, running a robot in the real world may be extremely costly and time consuming. Simulated training offers an appealing alternative, but ensuring that policies trained in simulation can transfer effectively into the real world requires additional machinery. Simulations may not match reality, and typically bridging the simulation-to-reality gap requires domain knowledge and task-specific engineering. We can automate this process by employing generative models to translate simulated images into realistic ones. However, this sort of translation is typically task-agnostic, in that the translated images may not preserve all features that are relevant to the task. In this paper, we introduce the RL-scene consistency loss for image translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image. This allows us to learn a task-aware translation. Incorporating this loss into unsupervised domain translation, we obtain the RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning. In evaluations of RL-CycleGAN on two vision-based robotics grasping tasks, we show that RL-CycleGAN offers a substantial improvement over a number of prior methods for sim-to-real transfer, attaining excellent real-world performance with only a modest number of real-world observations. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rao_RL-CycleGAN_Reinforcement_Learning_Aware_Simulation-to-Real_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rao_RL-CycleGAN_Reinforcement_Learning_Aware_Simulation-to-Real_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rao_RL-CycleGAN_Reinforcement_Learning_Aware_Simulation-to-Real_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rao_RL-CycleGAN_Reinforcement_Learning_CVPR_2020_supplemental.pdf | null | null |
Video Instance Segmentation Tracking With a Modified VAE Architecture | Chung-Ching Lin, Ying Hung, Rogerio Feris, Linglin He | We propose a modified variational autoencoder (VAE) architecture built on top of Mask R-CNN for instance-level video segmentation and tracking. The method builds a shared encoder and three parallel decoders, yielding three disjoint branches for predictions of future frames, object detection boxes, and instance segmentation masks. To effectively solve multiple learning tasks, we introduce a Gaussian Process model to enhance the statistical representation of VAE by relaxing the prior strong independent and identically distributed (iid) assumption of conventional VAEs and allowing potential correlations among extracted latent variables. The network learns embedded spatial interdependence and motion continuity in video data and creates a representation that is effective to produce high-quality segmentation masks and track multiple instances in diverse and unstructured videos. Evaluation on a variety of recently introduced datasets shows that our model outperforms previous methods and achieves the new best in class performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Video_Instance_Segmentation_Tracking_With_a_Modified_VAE_Architecture_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Video_Instance_Segmentation_Tracking_With_a_Modified_VAE_Architecture_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Video_Instance_Segmentation_Tracking_With_a_Modified_VAE_Architecture_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Video_Instance_Segmentation_CVPR_2020_supplemental.pdf | null | null |
High-Dimensional Convolutional Networks for Geometric Pattern Recognition | Christopher Choy, Junha Lee, Rene Ranftl, Jaesik Park, Vladlen Koltun | High-dimensional geometric patterns appear in many computer vision problems. In this work, we present high-dimensional convolutional networks for geometric pattern recognition problems that arise in 2D and 3D registration problems. We first propose high-dimensional convolutional networks from 4 to 32 dimensions and analyze the geometric pattern recognition capacity in high-dimensional linear regression problems. Next, we show that the 3D correspondences form hyper-surface in a 6-dimensional space and validate our network on 3D registration problems. Finally, we use image correspondences, which form a 4-dimensional hyper-conic section, and show that the high-dimensional convolutional networks are on par with many state-of-the-art multi-layered perceptrons. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choy_High-Dimensional_Convolutional_Networks_for_Geometric_Pattern_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.08144 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choy_High-Dimensional_Convolutional_Networks_for_Geometric_Pattern_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choy_High-Dimensional_Convolutional_Networks_for_Geometric_Pattern_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choy_High-Dimensional_Convolutional_Networks_CVPR_2020_supplemental.pdf | null | null |
AOWS: Adaptive and Optimal Network Width Search With Latency Constraints | Maxim Berman, Leonid Pishchulin, Ning Xu, Matthew B. Blaschko, Gerard Medioni | Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the target platform. We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware. We first show that we can use a black-box approach to estimate a realistic latency model for a specific inference platform, without the need for low-level access to the inference computation. Then, we design a pairwise MRF to score any channel configuration and use dynamic programming to efficiently decode the best performing configuration, yielding an optimal solution for the network width search. Finally, we propose an adaptive channel configuration sampling scheme to gradually specialize the training phase to the target computational constraints. Experiments on ImageNet classification show that our approach can find networks fitting the resource constraints on different target platforms while improving accuracy over the state-of-the-art efficient networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Berman_AOWS_Adaptive_and_Optimal_Network_Width_Search_With_Latency_Constraints_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.10481 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Berman_AOWS_Adaptive_and_Optimal_Network_Width_Search_With_Latency_Constraints_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Berman_AOWS_Adaptive_and_Optimal_Network_Width_Search_With_Latency_Constraints_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Berman_AOWS_Adaptive_and_CVPR_2020_supplemental.pdf | null | null |
Attentive Weights Generation for Few Shot Learning via Information Maximization | Yiluan Guo, Ngai-Man Cheung | Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning methods for few shot image classification due to its simplicity and effectiveness. In this work, we present Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which introduces two novel contributions: i) Mutual information maximization between generated weights and data within the task; this enables the generated weights to retain information of the task and the specific query sample. ii) Self-attention and cross-attention paths to encode the context of the task and individual queries. Both two contributions are shown to be very effective in extensive experiments. Overall, AWGIM is competitive with state-of-the-art. Code is available at https://github.com/Yiluan/AWGIM. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Attentive_Weights_Generation_for_Few_Shot_Learning_via_Information_Maximization_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=RL15VFYf6QY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Attentive_Weights_Generation_for_Few_Shot_Learning_via_Information_Maximization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Attentive_Weights_Generation_for_Few_Shot_Learning_via_Information_Maximization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Attentive_Weights_Generation_CVPR_2020_supplemental.pdf | null | null |
DeepLPF: Deep Local Parametric Filters for Image Enhancement | Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, Gregory Slabaugh | Digital artists often improve the aesthetic quality of digital photographs through manual retouching. Beyond global adjustments, professional image editing programs provide local adjustment tools operating on specific parts of an image. Options include parametric (graduated, radial filters) and unconstrained brush tools. These highly expressive tools enable a diverse set of local image enhancements. However, their use can be time consuming, and requires artistic capability. State-of-the-art automated image enhancement approaches typically focus on learning pixel-level or global enhancements. The former can be noisy and lack interpretability, while the latter can fail to capture fine-grained adjustments. In this paper, we introduce a novel approach to automatically enhance images using learned spatially local filters of three different types (Elliptical Filter, Graduated Filter, Polynomial Filter). We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image. DeepLPF provides a natural form of model regularization and enables interpretable, intuitive adjustments that lead to visually pleasing results. We report on multiple benchmarks and show that DeepLPF produces state-of-the-art performance on two variants of the MIT-Adobe 5k dataset, often using a fraction of the parameters required for competing methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Moran_DeepLPF_Deep_Local_Parametric_Filters_for_Image_Enhancement_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13985 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Moran_DeepLPF_Deep_Local_Parametric_Filters_for_Image_Enhancement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Moran_DeepLPF_Deep_Local_Parametric_Filters_for_Image_Enhancement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Moran_DeepLPF_Deep_Local_CVPR_2020_supplemental.pdf | null | null |
Joint Graph-Based Depth Refinement and Normal Estimation | Mattia Rossi, Mireille El Gheche, Andreas Kuhn, Pascal Frossard | Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenarios are characterized by a prevalence of human made structures, which in most of the cases are either inherently piece-wise planar or can be approximated as such. With these settings in mind, we devise a novel depth refinement framework that aims at recovering the underlying piece-wise planarity of those inverse depth maps associated to piece-wise planar scenes. We formulate this task as an optimization problem involving a data fidelity term, which minimizes the distance to the noisy and possibly incomplete input inverse depth map, as well as a regularization, which enforces a piece-wise planar solution. As for the regularization term, we model the inverse depth map pixels as the nodes of a weighted graph, with the weight of the edge between two pixels capturing the likelihood that they belong to the same plane in the scene. The proposed regularization fits a plane at each pixel automatically, avoiding any a priori estimation of the scene planes, and enforces that strongly connected pixels are assigned to the same plane. The resulting optimization problem is solved efficiently with the ADAM solver. Extensive tests show that our method leads to a significant improvement in depth refinement, both visually and numerically, with respect to state-of-the-art algorithms on the Middlebury, KITTI and ETH3D multi-view datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rossi_Joint_Graph-Based_Depth_Refinement_and_Normal_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01306 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rossi_Joint_Graph-Based_Depth_Refinement_and_Normal_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rossi_Joint_Graph-Based_Depth_Refinement_and_Normal_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Recognizing Objects From Any View With Object and Viewer-Centered Representations | Sainan Liu, Vincent Nguyen, Isaac Rehg, Zhuowen Tu | In this paper, we tackle an important task in computer vision: any view object recognition. In both training and testing, for each object instance, we are only given its 2D image viewed from an unknown angle. We propose a computational framework by designing object and viewer-centered neural networks (OVCNet) to recognize an object instance viewed from an arbitrary unknown angle. OVCNet consists of three branches that respectively implement object-centered, 3D viewer-centered, and in-plane viewer-centered recognition. We evaluate our proposed OVCNet using two metrics with unseen views from both seen and novel object instances. Experimental results demonstrate the advantages of OVCNet over classic 2D-image-based CNN classifiers, 3D-object (inferred from 2D image) classifiers, and competing multi-view based approaches. It gives rise to a viable and practical computing framework that combines both viewpoint-dependent and viewpoint-independent features for object recognition from any view. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Recognizing_Objects_From_Any_View_With_Object_and_Viewer-Centered_Representations_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=VsNxsec1K3g | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Recognizing_Objects_From_Any_View_With_Object_and_Viewer-Centered_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Recognizing_Objects_From_Any_View_With_Object_and_Viewer-Centered_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Recognizing_Objects_From_CVPR_2020_supplemental.pdf | null | null |
Learning to Segment the Tail | Xinting Hu, Yi Jiang, Kaihua Tang, Jingyuan Chen, Chunyan Miao, Hanwang Zhang | Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data. We propose a "divide&conquer" strategy for the challenging LVIS task: divide the whole data into balanced parts and then apply incremental learning to conquer each one. This derives a novel learning paradigm: class-incremental few-shot learning, which is especially effective for the challenge evolving over time: 1) the class imbalance among the old class knowledge review and 2) the few-shot data in new-class learning. We call our approach Learning to Segment the Tail (LST). In particular, we design an instance-level balanced replay scheme, which is a memory-efficient approximation to balance the instance-level samples from the old-class images. We also propose to use a meta-module for new-class learning, where the module parameters are shared across incremental phases, gaining the learning-to-learn knowledge incrementally, from the data-rich head to the data-poor tail. We empirically show that: at the expense of a little sacrifice of head-class forgetting, we can gain a significant 8.3% AP improvement for the tail classes with less than 10 instances, achieving an overall 2.0% AP boost for the whole 1,230 classes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_Learning_to_Segment_the_Tail_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00900 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Learning_to_Segment_the_Tail_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Learning_to_Segment_the_Tail_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks | Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, Dieter Fox | We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. ALFRED includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like "Rinse off a mug and place it in the coffee maker." and low-level language instructions like "Walk to the coffee maker on the right." ALFRED tasks are more complex in terms of sequence length, action space, and language than existing vision- and-language task datasets. We show that a baseline model based on recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shridhar_ALFRED_A_Benchmark_for_Interpreting_Grounded_Instructions_for_Everyday_Tasks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01734 | https://www.youtube.com/watch?v=_Qamhop2JDU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shridhar_ALFRED_A_Benchmark_for_Interpreting_Grounded_Instructions_for_Everyday_Tasks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shridhar_ALFRED_A_Benchmark_for_Interpreting_Grounded_Instructions_for_Everyday_Tasks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shridhar_ALFRED_A_Benchmark_CVPR_2020_supplemental.zip | https://cove.thecvf.com/datasets/336 | null |
Robust Object Detection Under Occlusion With Context-Aware CompositionalNets | Angtian Wang, Yihong Sun, Adam Kortylewski, Alan L. Yuille | Detecting partially occluded objects is a difficult task. Our experimental results show that deep learning approaches, such as Faster R-CNN, are not robust at object detection under occlusion. Compositional convolutional neural networks (CompositionalNets) have been shown to be robust at classifying occluded objects by explicitly representing the object as a composition of parts. In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself. Under strong object occlusion, the influence of the context is amplified which can have severe negative effects for detection at test time. In order to overcome this, we propose to segment the context during training via bounding box annotations. We then use the segmentation to learn a context-aware compositionalNet that disentangles the representation of the context and the object. 2) We extend the part-based voting scheme in CompositionalNets to vote for the corners of the object's bounding box, which enables the model to reliably estimate bounding boxes for partially occluded objects. Our extensive experiments show that our proposed model can detect objects robustly, increasing the detection performance of strongly occluded vehicles from PASCAL3D+ and MS-COCO by 41% and 35% respectively in absolute performance relative to Faster R-CNN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Robust_Object_Detection_Under_Occlusion_With_Context-Aware_CompositionalNets_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.11643 | https://www.youtube.com/watch?v=XalAhF8Bi_0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Robust_Object_Detection_Under_Occlusion_With_Context-Aware_CompositionalNets_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Robust_Object_Detection_Under_Occlusion_With_Context-Aware_CompositionalNets_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Robust_Object_Detection_CVPR_2020_supplemental.pdf | null | null |
MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices | Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adam, Quoc V. Le | Despite the blooming success of architecture search for vision tasks in resource-constrained environments, the design of on-device object detection architectures have mostly been manual. The few automated search efforts are either centered around non-mobile-friendly search spaces or not guided by on-device latency. We propose MnasFPN, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models. The learned MnasFPN head, when paired with MobileNetV2 body, outperforms MobileNetV3+SSDLite by 1.8 mAP at similar latency on Pixel. It is both 1 mAP more accurate and 10% faster than NAS-FPNLite. Ablation studies show that the majority of the performance gain comes from innovations in the search space. Further explorations reveal an interesting coupling between the search space design and the search algorithm, for which the complexity of MnasFPN search space is opportune. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_MnasFPN_Learning_Latency-Aware_Pyramid_Architecture_for_Object_Detection_on_Mobile_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01106 | https://www.youtube.com/watch?v=Am9njSwoHEE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_MnasFPN_Learning_Latency-Aware_Pyramid_Architecture_for_Object_Detection_on_Mobile_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_MnasFPN_Learning_Latency-Aware_Pyramid_Architecture_for_Object_Detection_on_Mobile_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation | Sunghun Joung, Seungryong Kim, Hanjae Kim, Minsu Kim, Ig-Jae Kim, Junghyun Cho, Kwanghoon Sohn | Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have limited ability to severe object viewpoint changes. To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space. CCNs extract a view-specific feature through a view-specific convolutional kernel to predict object category scores at each viewpoint. With the view-specific feature, we simultaneously determine objective category and viewpoints using the proposed sinusoidal soft-argmax module. Our experiments demonstrate the effectiveness of the cylindrical convolutional networks on joint object detection and viewpoint estimation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Joung_Cylindrical_Convolutional_Networks_for_Joint_Object_Detection_and_Viewpoint_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11303 | https://www.youtube.com/watch?v=zHoYO8XOpsk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Joung_Cylindrical_Convolutional_Networks_for_Joint_Object_Detection_and_Viewpoint_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Joung_Cylindrical_Convolutional_Networks_for_Joint_Object_Detection_and_Viewpoint_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Straight to the Point: Fast-Forwarding Videos via Reinforcement Learning Using Textual Data | Washington Ramos, Michel Silva, Edson Araujo, Leandro Soriano Marcolino, Erickson Nascimento | The rapid increase in the amount of published visual data and the limited time of users bring the demand for processing untrimmed videos to produce shorter versions that convey the same information. Despite the remarkable progress that has been made by summarization methods, most of them can only select a few frames or skims, which creates visual gaps and breaks the video context. In this paper, we present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos. Our approach can adaptively select frames that are not relevant to convey the information without creating gaps in the final video. Our agent is textually and visually oriented to select which frames to remove to shrink the input video. Additionally, we propose a novel network, called Visually-guided Document Attention Network (VDAN), able to generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in terms of F1 Score and coverage at the video segment level. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ramos_Straight_to_the_Point_Fast-Forwarding_Videos_via_Reinforcement_Learning_Using_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14229 | https://www.youtube.com/watch?v=mumJdoZlEQQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramos_Straight_to_the_Point_Fast-Forwarding_Videos_via_Reinforcement_Learning_Using_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramos_Straight_to_the_Point_Fast-Forwarding_Videos_via_Reinforcement_Learning_Using_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ramos_Straight_to_the_CVPR_2020_supplemental.zip | null | null |
SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings | Wenyu Han, Siyuan Xiang, Chenhui Liu, Ruoyu Wang, Chen Feng | Spatial reasoning is an important component of human intelligence. We can imagine the shapes of 3D objects and reason about their spatial relations by merely looking at their three-view line drawings in 2D, with different levels of competence. Can deep networks be trained to perform spatial reasoning tasks? How can we measure their "spatial intelligence"? To answer these questions, we present the SPARE3D dataset. Based on cognitive science and psychometrics, SPARE3D contains three types of 2D-3D reasoning tasks on view consistency, camera pose, and shape generation, with increasing difficulty. We then design a method to automatically generate a large number of challenging questions with ground truth answers for each task. They are used to provide supervision for training our baseline models using state-of-the-art architectures like ResNet. Our experiments show that although convolutional networks have achieved superhuman performance in many visual learning tasks, their spatial reasoning performance in SPARE3D is almost equal to random guesses. We hope SPARE3D can stimulate new problem formulations and network designs for spatial reasoning to empower intelligent robots to operate effectively in the 3D world via 2D sensors. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Han_SPARE3D_A_Dataset_for_SPAtial_REasoning_on_Three-View_Line_Drawings_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14034 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_SPARE3D_A_Dataset_for_SPAtial_REasoning_on_Three-View_Line_Drawings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_SPARE3D_A_Dataset_for_SPAtial_REasoning_on_Three-View_Line_Drawings_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Han_SPARE3D_A_Dataset_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/353 | null |
Learning the Redundancy-Free Features for Generalized Zero-Shot Object Recognition | Zongyan Han, Zhenyong Fu, Jian Yang | Zero-shot object recognition or zero-shot learning aims to transfer the object recognition ability among the semantically related categories, such as fine-grained animal or bird species. However, the images of different fine-grained objects tend to merely exhibit subtle differences in appearance, which will severely deteriorate zero-shot object recognition. To reduce the superfluous information in the fine-grained objects, in this paper, we propose to learn the redundancy-free features for generalized zero-shot learning. We achieve our motivation by projecting the original visual features into a new (redundancy-free) feature space and then restricting the statistical dependence between these two feature spaces. Furthermore, we require the projected features to keep and even strengthen the category relationship in the redundancy-free feature space. In this way, we can remove the redundant information from the visual features without losing the discriminative information. We extensively evaluate the performance on four benchmark datasets. The results show that our redundancy-free feature based generalized zero-shot learning (RFF-GZSL) approach can outperform the state-of-the-arts often by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Han_Learning_the_Redundancy-Free_Features_for_Generalized_Zero-Shot_Object_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.08939 | https://www.youtube.com/watch?v=k0846_jYKDY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_Learning_the_Redundancy-Free_Features_for_Generalized_Zero-Shot_Object_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_Learning_the_Redundancy-Free_Features_for_Generalized_Zero-Shot_Object_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | https://openaccess.thecvf.com |
Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection | Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Yong Jae Lee, Alexander G. Schwing, Jan Kautz | Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption. Addressing these challenges is difficult, as it often requires to eliminate uncertainties and trivial solutions. To target these issues we develop an instance-aware and context-focused unified framework. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method achieves state-of-the-art results on COCO (12.1% AP, 24.8% AP50), VOC 2007 (54.9% AP), and VOC 2012 (52.1% AP), improving baselines by great margins. In addition, the proposed method is the first to benchmark ResNet based models and weakly supervised video object detection. Refer to our project page for code, models, and more details: https://github.com/NVlabs/wetectron. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ren_Instance-Aware_Context-Focused_and_Memory-Efficient_Weakly_Supervised_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04725 | https://www.youtube.com/watch?v=GhekowXS3r4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ren_Instance-Aware_Context-Focused_and_Memory-Efficient_Weakly_Supervised_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ren_Instance-Aware_Context-Focused_and_Memory-Efficient_Weakly_Supervised_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ren_Instance-Aware_Context-Focused_and_CVPR_2020_supplemental.pdf | null | null |
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation | Zhonghao Wang, Mo Yu, Yunchao Wei, Rogerio Feris, Jinjun Xiong, Wen-mei Hwu, Thomas S. Huang, Honghui Shi | We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work. State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. Based on the observation that stuff categories usually share similar appearances across images of different domains while things (i.e. object instances) have much larger differences, we propose to improve the semantic-level alignment with different strategies for stuff regions and for things: 1) for the stuff categories, we generate feature representation for each class and conduct the alignment operation from the target domain to the source domain; 2) for the thing categories, we generate feature representation for each individual instance and encourage the instance in the target domain to align with the most similar one in the source domain. In this way, the individual differences within thing categories will also be considered to alleviate over-alignment. In addition to our proposed method, we further reveal the reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy and show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains. We conduct extensive experiments in two unsupervised domain adaptation tasks, i.e. GTA5 - Cityscapes and SYNTHIA - Cityscapes, and achieve the new state-of-the-art segmentation accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Differential_Treatment_for_Stuff_and_Things_A_Simple_Unsupervised_Domain_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08040 | https://www.youtube.com/watch?v=1sPGSh4ZWYs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Differential_Treatment_for_Stuff_and_Things_A_Simple_Unsupervised_Domain_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Differential_Treatment_for_Stuff_and_Things_A_Simple_Unsupervised_Domain_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention | Xuesong Chen, Xiyu Yan, Feng Zheng, Yong Jiang, Shu-Tao Xia, Yong Zhao, Rongrong Ji | Almost all adversarial attacks in computer vision are aimed at pre-known object categories, which could be offline trained for generating perturbations. But as for visual object tracking, the tracked target categories are normally unknown in advance. However, the tracking algorithms also have potential risks of being attacked, which could be maliciously used to fool the surveillance systems. Meanwhile, it is still a challenging task that adversarial attacks on tracking since it has the free-model tracked target. Therefore, to help draw more attention to the potential risks, we study adversarial attacks on tracking algorithms. In this paper, we propose a novel one-shot adversarial attack method to generate adversarial examples for free-model single object tracking, where merely adding slight perturbations on the target patch in the initial frame causes state-of-the-art trackers to lose the target in subsequent frames. Specifically, the optimization objective of the proposed attack consists of two components and leverages the dual attention mechanisms. The first component adopts a targeted attack strategy by optimizing the batch confidence loss with confidence attention while the second one applies a general perturbation strategy by optimizing the feature loss with channel attention. Experimental results show that our approach can significantly lower the accuracy of the most advanced Siamese network-based trackers on three benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_One-Shot_Adversarial_Attacks_on_Visual_Tracking_With_Dual_Attention_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_One-Shot_Adversarial_Attacks_on_Visual_Tracking_With_Dual_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_One-Shot_Adversarial_Attacks_on_Visual_Tracking_With_Dual_Attention_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Video Object Grounding Using Semantic Roles in Language Description | Arka Sadhu, Kan Chen, Ram Nevatia | We explore the task of Video Object Grounding (VOG), which grounds objects in videos referred to in natural language descriptions. Previous methods apply image grounding based algorithms to address VOG, fail to explore the object relation information and suffer from limited generalization. Here, we investigate the role of object relations in VOG and propose a novel framework VOGNet to encode multi-modal object relations via self-attention with relative position encoding. To evaluate VOGNet, we propose novel contrasting sampling methods to generate more challenging grounding input samples, and construct a new dataset called ActivityNet-SRL (ASRL) based on existing caption and grounding datasets. Experiments on ASRL validate the need of encoding object relations in VOG, and our VOGNet outperforms competitive baselines by a significant margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sadhu_Video_Object_Grounding_Using_Semantic_Roles_in_Language_Description_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10606 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sadhu_Video_Object_Grounding_Using_Semantic_Roles_in_Language_Description_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sadhu_Video_Object_Grounding_Using_Semantic_Roles_in_Language_Description_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sadhu_Video_Object_Grounding_CVPR_2020_supplemental.pdf | null | null |
Context Prior for Scene Segmentation | Changqian Yu, Jingbo Wang, Changxin Gao, Gang Yu, Chunhua Shen, Nong Sang | Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this work, we directly supervise the feature aggregation to distinguish the intra-class and interclass context clearly. Specifically, we develop a Context Prior with the supervision of the Affinity Loss. Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior. The learned Context Prior extracts the pixels belonging to the same category, while the reversed prior focuses on the pixels of different classes. Embedded into a conventional deep CNN, the proposed Context Prior Layer can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation. To validate the effectiveness, we design an effective Context Prior Network (CPNet). Extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against state-of-the-art semantic segmentation approaches. More specifically, our algorithm achieves 46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on Cityscapes. Code is available at https://git.io/ContextPrior. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Context_Prior_for_Scene_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01547 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Context_Prior_for_Scene_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Context_Prior_for_Scene_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Binarizing MobileNet via Evolution-Based Searching | Hai Phan, Zechun Liu, Dang Huynh, Marios Savvides, Kwang-Ting Cheng, Zhiqiang Shen | Binary Neural Networks (BNNs), known to be one among the effectively compact network architectures, have achieved great outcomes in the visual tasks. Designing efficient binary architectures is not trivial due to the binary nature of the network. In this paper, we propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet, a compact network with separable depth-wise convolution. Being inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an approximately optimal trade-off between computational cost and model accuracy. Our objective is to come up with a tiny yet efficient binary neural architecture by exploring the best candidates of the group convolution while optimizing the model performance in terms of complexity and latency. The approach is threefold. First, we modify and train strong baseline binary networks with a wide range of random group combinations at each convolutional layer. This set-up gives the binary neural networks a capability of preserving essential information through layers. Second, to find a good set of hyper-parameters for group convolutions we make use of the evolutionary search which leverages the exploration of efficient 1-bit models. Lastly, these binary models are trained from scratch in a usual manner to achieve the final binary model. Various experiments on ImageNet are conducted to show that following our construction guideline, the final model achieves 60.09% Top-1 accuracy and outperforms the state-of-the-art CI-BCNN with the same computational cost. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Phan_Binarizing_MobileNet_via_Evolution-Based_Searching_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.06305 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Phan_Binarizing_MobileNet_via_Evolution-Based_Searching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Phan_Binarizing_MobileNet_via_Evolution-Based_Searching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Adaptive Hierarchical Down-Sampling for Point Cloud Classification | Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani, Bingbing Liu, Jun Luo | Deterministic down-sampling of an unordered point cloud in a deep neural network has not been rigorously studied so far. Existing methods down-sample the points regardless of their importance for the network output and often address down-sampling the raw point cloud before processing. As a result, some important points in the point cloud may be removed, while less valuable points may be passed to next layers. In contrast, the proposed adaptive down-sampling method samples the points by taking into account the importance of each point, which varies according to application, task and training data. In this paper, we propose a novel deterministic, adaptive, permutation-invariant down-sampling layer, called Critical Points Layer (CPL), which learns to reduce the number of points in an unordered point cloud while retaining the important (critical) ones. Unlike most graph-based point cloud down-sampling methods that use k-NN to find the neighboring points, CPL is a global down-sampling method, rendering it computationally very efficient. The proposed layer can be used along with a graph-based point cloud convolution layer to form a convolutional neural network, dubbed CP-Net in this paper. We introduce a CP-Net for 3D object classification that achieves high accuracy for the ModelNet 40 dataset among point cloud-based methods, which validates the effectiveness of the CPL. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nezhadarya_Adaptive_Hierarchical_Down-Sampling_for_Point_Cloud_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.08506 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Nezhadarya_Adaptive_Hierarchical_Down-Sampling_for_Point_Cloud_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Nezhadarya_Adaptive_Hierarchical_Down-Sampling_for_Point_Cloud_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Multi-View Camera Relocalization With Graph Neural Networks | Fei Xue, Xin Wu, Shaojun Cai, Junqiu Wang | We propose to construct a view graph to excavate the information of the whole given sequence for absolute camera pose estimation. Specifically, we harness GNNs to model the graph, allowing even non-consecutive frames to exchange information with each other. Rather than adopting the regular GNNs directly, we redefine the nodes, edges, and embedded functions to fit the relocalization task. Redesigned GNNs cooperate with CNNs in guiding knowledge propagation and feature extraction respectively to process multi-view high-dimension image features iteratively at different levels. Besides, a general graph-based loss function beyond constraints between consecutive views is employed for training the network in an end-to-end fashion. Extensive experiments conducted on both indoor and outdoor datasets demonstrate that our method outperforms previous approaches especially in large-scale and challenging scenarios. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xue_Learning_Multi-View_Camera_CVPR_2020_supplemental.pdf | null | null |
Distortion Agnostic Deep Watermarking | Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, Peyman Milanfar | Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference with the original image. Recently, deep learning-based methods achieved impressive results in both visual quality and message payload under a wide variety of image distortions. However, these methods all require differentiable models for the image distortions at training time, and may generalize poorly to unknown distortions. This is undesirable since the types of distortions applied to watermarked images are usually unknown and non-differentiable. In this paper, we propose a new framework for distortion-agnostic watermarking, where the image distortion is not explicitly modeled during training. Instead, the robustness of our system comes from two sources: adversarial training and channel coding. Compared to training on a fixed set of distortions and noise levels, our method achieves comparable or better results on distortions available during training, and better performance overall on unknown distortions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Distortion_Agnostic_Deep_Watermarking_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.04580 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Distortion_Agnostic_Deep_Watermarking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Distortion_Agnostic_Deep_Watermarking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Luo_Distortion_Agnostic_Deep_CVPR_2020_supplemental.pdf | null | null |
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation | Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao | In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision. The code will be available at https://github.com/zju3dv/disprcnn. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=KnslISMQBlQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Episode-Based Prototype Generating Network for Zero-Shot Learning | Yunlong Yu, Zhong Ji, Jungong Han, Zhongfei Zhang | We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics. During training, the model is trained within a collection of episodes, each of which is designed to simulate a zero-shot classification task. Through training multiple episodes, the model progressively accumulates ensemble experiences on predicting the mimetic unseen classes, which will generalize well on the real unseen classes. Based on this training framework, we propose a novel generative model that synthesizes visual prototypes conditioned on the class semantic prototypes. The proposed model aligns the visual-semantic interactions by formulating both the visual prototype generation and the class semantic inference into an adversarial framework paired with a parameter-economic Multi-modal Cross-Entropy Loss to capture the discriminative information. Extensive experiments on four datasets under both traditional ZSL and generalized ZSL tasks show that our model outperforms the state-of-the-art approaches by large margins. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Episode-Based_Prototype_Generating_Network_for_Zero-Shot_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.03360 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Episode-Based_Prototype_Generating_Network_for_Zero-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Episode-Based_Prototype_Generating_Network_for_Zero-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-Based Person Re-Identification | Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Zhibo Chen | Video-based person re-identification (reID) aims at matching the same person across video clips. It is a challenging task due to the existence of redundancy among frames, newly revealed appearance, occlusion, and motion blurs. In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation. In order to determine the contribution/importance of a spatial-temporal feature node, we propose to learn the attention from a global view with convolutional operations. Specifically, we stack its relations, i.e.no, pairwise correlations with respect to a representative set of reference feature nodes (S-RFNs) that represents global video information, together with the feature itself to infer the attention. Moreover, to exploit the semantics of different levels, we propose to learn multi-granularity attentions based on the relations captured at different granularities. Extensive ablation studies demonstrate the effectiveness of our attentive feature aggregation module MG-RAFA. Our framework achieves the state-of-the-art performance on three benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Multi-Granularity_Reference-Aided_Attentive_Feature_Aggregation_for_Video-Based_Person_Re-Identification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12224 | https://www.youtube.com/watch?v=c5XFzJRsDEw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Multi-Granularity_Reference-Aided_Attentive_Feature_Aggregation_for_Video-Based_Person_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Multi-Granularity_Reference-Aided_Attentive_Feature_Aggregation_for_Video-Based_Person_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Multi-Granularity_Reference-Aided_Attentive_CVPR_2020_supplemental.pdf | null | null |
Semi-Supervised Semantic Segmentation With Cross-Consistency Training | Yassine Ouali, Celine Hudelot, Myriam Tami | In this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation. Consistency training has proven to be a powerful semi-supervised learning framework for leveraging unlabeled data under the cluster assumption, in which the decision boundary should lie in low-density regions. In this work, we first observe that for semantic segmentation, the low-density regions are more apparent within the hidden representations than within the inputs. We thus propose cross-consistency training, where an invariance of the predictions is enforced over different perturbations applied to the outputs of the encoder. Concretely, a shared encoder and a main decoder are trained in a supervised manner using the available labeled examples. To leverage the unlabeled examples, we enforce a consistency between the main decoder predictions and those of the auxiliary decoders, taking as inputs different perturbed versions of the encoder's output, and consequently, improving the encoder's representations. The proposed method is simple and can easily be extended to use additional training signal, such as image-level labels or pixel-level labels across different domains. We perform an ablation study to tease apart the effectiveness of each component, and conduct extensive experiments to demonstrate that our method achieves state-of-the-art results in several datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ouali_Semi-Supervised_Semantic_Segmentation_With_Cross-Consistency_Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09005 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ouali_Semi-Supervised_Semantic_Segmentation_With_Cross-Consistency_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ouali_Semi-Supervised_Semantic_Segmentation_With_Cross-Consistency_Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ouali_Semi-Supervised_Semantic_Segmentation_CVPR_2020_supplemental.pdf | null | null |
GaitPart: Temporal Part-Based Model for Gait Recognition | Chao Fan, Yunjie Peng, Chunshui Cao, Xu Liu, Saihui Hou, Jiannan Chi, Yongzhen Huang, Qing Li, Zhiqiang He | Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies. At present, most gait recognition methods take the whole human body as a unit to establish the spatio-temporal representations. However, we have observed that different parts of human body possess evidently various visual appearances and movement patterns during walking. In the latest literature, employing partial features for human body description has been verified being beneficial to individual recognition. Taken above insights together, we assume that each part of human body needs its own spatio-temporal expression. Then, we propose a novel part-based model GaitPart and get two aspects effect of boosting the performance: On the one hand, Focal Convolution Layer, a new applying of convolution, is presented to enhance the fine-grained learning of the part-level spatial features. On the other hand, the Micro-motion Capture Module (MCM) is proposed and there are several parallel MCMs in the GaitPart corresponding to the pre-defined parts of the human body, respectively. It is worth mentioning that the MCM is a novel way of temporal modeling for gait task, which focuses on the short-range temporal features rather than the redundant long-range features for cycle gait. Experiments on two of the most popular public datasets, CASIA-B and OU-MVLP, richly exemplified that our method meets a new state-of-the-art on multiple standard benchmarks. The source code will be available on https://github.com/ChaoFan96/GaitPart. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack | Zhezhi He, Adnan Siraj Rakin, Jingtao Li, Chaitali Chakrabarti, Deliang Fan | Recently, a new paradigm of the adversarial attack on the quantized neural network weights has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka. Bit-Flip Attack (BFA). BFA has shown extraordinary attacking ability, where the adversary can malfunction a quantized Deep Neural Network (DNN) as a random guess, through malicious bit-flips on a small set of vulnerable weight bits (e.g., 13 out of 93 millions bits of 8-bit quantized ResNet-18). However, there are no effective defensive methods to enhance the fault-tolerance capability of DNN against such BFA. In this work, we conduct comprehensive investigations on BFA and propose to leverage binarization-aware training and its relaxation -- piece-wise clustering as simple and effective countermeasures to BFA. The experiments show that, for BFA to achieve the identical prediction accuracy degradation (e.g., below 11% on CIFAR-10), it requires 19.3x and 480.1x more effective malicious bit-flips on ResNet-20 and VGG-11 respectively, compared to defend-free counterparts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/He_Defending_and_Harnessing_the_Bit-Flip_Based_Adversarial_Weight_Attack_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Defending_and_Harnessing_the_Bit-Flip_Based_Adversarial_Weight_Attack_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Defending_and_Harnessing_the_Bit-Flip_Based_Adversarial_Weight_Attack_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Dense Regression Network for Video Grounding | Runhao Zeng, Haoming Xu, Wenbing Huang, Peihao Chen, Mingkui Tan, Chuang Gan | We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy. Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query. We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zeng_Dense_Regression_Network_for_Video_Grounding_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03545 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Dense_Regression_Network_for_Video_Grounding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Dense_Regression_Network_for_Video_Grounding_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zeng_Dense_Regression_Network_CVPR_2020_supplemental.pdf | null | null |
TITAN: Future Forecast Using Action Priors | Srikanth Malla, Behzad Dariush, Chiho Choi | We consider the problem of predicting the future trajectory of scene agents from egocentric views obtained from a moving platform. This problem is important in a variety of domains, particularly for autonomous systems making reactive or strategic decisions in navigation. In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion. In the absence of an appropriate dataset for this task, we created the TITAN dataset that consists of 700 labeled video-clips (with odometry) captured from a moving vehicle on highly interactive urban traffic scenes in Tokyo. Our dataset includes 50 labels including vehicle states and actions, pedestrian age groups, and targeted pedestrian action attributes that are organized hierarchically corresponding to atomic, simple/complex-contextual, transportive, and communicative actions. To evaluate our model, we conducted extensive experiments on the TITAN dataset, revealing significant performance improvement against baselines and state-of-the-art algorithms. We also report promising results from our Agent Importance Mechanism (AIM), a module which provides insight into assessment of perceived risk by calculating the relative influence of each agent on the future ego-trajectory. The dataset is available at https://usa.honda-ri.com/titan | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Malla_TITAN_Future_Forecast_Using_Action_Priors_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13886 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Malla_TITAN_Future_Forecast_Using_Action_Priors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Malla_TITAN_Future_Forecast_Using_Action_Priors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Malla_TITAN_Future_Forecast_CVPR_2020_supplemental.zip | null | null |
Camera On-Boarding for Person Re-Identification Using Hypothesis Transfer Learning | Sk Miraj Ahmed, Aske R. Lejbolle, Rameswar Panda, Amit K. Roy-Chowdhury | Most of the existing approaches for person re-identification consider a static setting where the number of cameras in the network is fixed. An interesting direction, which has received little attention, is to explore the dynamic nature of a camera network, where one tries to adapt the existing re-identification models after on-boarding new cameras, with little additional effort. There have been a few recent methods proposed in person re-identification that attempt to address this problem by assuming the labeled data in the existing network is still available while adding new cameras. This is a strong assumption since there may exist some privacy issues for which one may not have access to those data. Rather, based on the fact that it is easy to store the learned re-identifications models, which mitigates any data privacy concern, we develop an efficient model adaptation approach using hypothesis transfer learning that aims to transfer the knowledge using only source models and limited labeled data, but without using any source camera data from the existing network. Our approach minimizes the effect of negative transfer by finding an optimal weighted combination of multiple source models for transferring the knowledge. Extensive experiments on four challenging benchmark datasets with variable number of cameras well demonstrate the efficacy of our proposed approach over state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ahmed_Camera_On-Boarding_for_Person_Re-Identification_Using_Hypothesis_Transfer_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.11149 | https://www.youtube.com/watch?v=nBWlJ0xHLa8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ahmed_Camera_On-Boarding_for_Person_Re-Identification_Using_Hypothesis_Transfer_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ahmed_Camera_On-Boarding_for_Person_Re-Identification_Using_Hypothesis_Transfer_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ahmed_Camera_On-Boarding_for_CVPR_2020_supplemental.pdf | null | https://openaccess.thecvf.com |
EfficientDet: Scalable and Efficient Object Detection | Mingxing Tan, Ruoming Pang, Quoc V. Le | Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x - 9x smaller and using 13x - 42x fewer FLOPs than previous detector. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tan_EfficientDet_Scalable_and_Efficient_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09070 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tan_EfficientDet_Scalable_and_Efficient_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tan_EfficientDet_Scalable_and_Efficient_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations | Chaoning Zhang, Philipp Benz, Tooba Imtiaz, In So Kweon | A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a vector for feature representation, and exploit them to analyze the mutual influence of two independent inputs based on the Pearson correlation coefficient (PCC). We utilize this vector representation to understand adversarial examples by disentangling the clean images and adversarial perturbations, and analyze their influence on each other. Our results suggest a new perspective towards the relationship between images and universal perturbations: Universal perturbations contain dominant features, and images behave like noise to them. This feature perspective leads to a new method for generating targeted universal adversarial perturbations using random source images. We are the first to achieve the challenging task of a targeted universal attack without utilizing original training data. Our approach using a proxy dataset achieves comparable performance to the state-of-the-art baselines which utilize the original training dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Understanding_Adversarial_Examples_From_the_Mutual_Influence_of_Images_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2007.06189 | https://www.youtube.com/watch?v=9ca4xvfAZbs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Understanding_Adversarial_Examples_From_the_Mutual_Influence_of_Images_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Understanding_Adversarial_Examples_From_the_Mutual_Influence_of_Images_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Understanding_Adversarial_Examples_CVPR_2020_supplemental.pdf | null | null |
Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS | Gabriel Bender, Hanxiao Liu, Bo Chen, Grace Chu, Shuyang Cheng, Pieter-Jan Kindermans, Quoc V. Le | Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models. There is, however, an ongoing debate whether these efficient methods are significantly better than random search. Here we perform a thorough comparison between efficient and random search methods on a family of progressively larger and more challenging search spaces for image classification and detection on ImageNet and COCO. While the efficacies of both methods are problem-dependent, our experiments demonstrate that there are large, realistic tasks where efficient search methods can provide substantial gains over random search. In addition, we propose and evaluate techniques which improve the quality of searched architectures and reduce the need for manual hyper-parameter tuning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.pdf | http://arxiv.org/abs/2008.06120 | https://www.youtube.com/watch?v=36PSrtqqKWE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bender_Can_Weight_Sharing_CVPR_2020_supplemental.pdf | null | null |
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