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RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network | Bing Han, Gopalakrishnan Srinivasan, Kaushik Roy | Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven data analytics. The best performing SNNs for image recognition tasks are obtained by converting a trained Analog Neural Network (ANN), consisting of Rectified Linear Units (ReLU), to SNN composed of integrate-and-fire neurons with "proper" firing thresholds. The converted SNNs typically incur loss in accuracy compared to that provided by the original ANN and require sizable number of inference time-steps to achieve the best accuracy. We find that performance degradation in the converted SNN stems from using "hard reset" spiking neuron that is driven to fixed reset potential once its membrane potential exceeds the firing threshold, leading to information loss during SNN inference. We propose ANN-SNN conversion using "soft reset" spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the "residual" membrane potential above threshold at the firing instants. We demonstrate near loss-less ANN-SNN conversion using RMP neurons for VGG-16, ResNet-20, and ResNet-34 SNNs on challenging datasets including CIFAR-10 (93.63% top-1), CIFAR-100 (70.93% top-1), and ImageNet (73.09% top-1 accuracy). Our results also show that RMP-SNN surpasses the best inference accuracy provided by the converted SNN with "hard reset" spiking neurons using 2-8 times fewer inference time-steps across network architectures and datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Han_RMP-SNN_Residual_Membrane_Potential_Neuron_for_Enabling_Deeper_High-Accuracy_and_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=IsAqBi3QniA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_RMP-SNN_Residual_Membrane_Potential_Neuron_for_Enabling_Deeper_High-Accuracy_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Han_RMP-SNN_Residual_Membrane_Potential_Neuron_for_Enabling_Deeper_High-Accuracy_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Adversarial Feature Hallucination Networks for Few-Shot Learning | Kai Li, Yulun Zhang, Kunpeng Li, Yun Fu | The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL), which aims to learn concept of new classes with a few labeled samples. A natural approach to FSL is data augmentation and many recent works have proved the feasibility by proposing various data synthesis models. However, these models fail to well secure the discriminability and diversity of the synthesized data and thus often produce undesirable results. In this paper, we propose Adversarial Feature Hallucination Networks (AFHN) which is based on conditional Wasserstein Generative Adversarial networks (cWGAN) and hallucinates diverse and discriminative features conditioned on the few labeled samples. Two novel regularizers, i.e., the classification regularizer and the anti-collapse regularizer, are incorporated into AFHN to encourage discriminability and diversity of the synthesized features, respectively. Ablation study verifies the effectiveness of the proposed cWGAN based feature hallucination framework and the proposed regularizers. Comparative results on three common benchmark datasets substantiate the superiority of AFHN to existing data augmentation based FSL approaches and other state-of-the-art ones. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Adversarial_Feature_Hallucination_Networks_for_Few-Shot_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13193 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Adversarial_Feature_Hallucination_Networks_for_Few-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Adversarial_Feature_Hallucination_Networks_for_Few-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image Forensics | Quentin Bammey, Rafael Grompone von Gioi, Jean-Michel Morel | Automatically finding suspicious regions in a potentially forged image by splicing, inpainting or copy-move remains a widely open problem. Blind detection neural networks trained on benchmark data are flourishing. Yet, these methods do not provide an explanation of their detections. The more traditional methods try to provide such evidence by pointing out local inconsistencies in the image noise, JPEG compression, chromatic aberration, or in the mosaic. In this paper we develop a blind method that can train directly on unlabelled and potentially forged images to point out local mosaic inconsistencies. To this aim we designed a CNN structure inspired from demosaicing algorithms and directed at classifying image blocks by their position in the image modulo (2 x 2). Creating a diversified benchmark database using varied demosaicing methods, we explore the efficiency of the method and its ability to adapt quickly to any new data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bammey_An_Adaptive_Neural_Network_for_Unsupervised_Mosaic_Consistency_Analysis_in_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=e-cjyuswBJg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bammey_An_Adaptive_Neural_Network_for_Unsupervised_Mosaic_Consistency_Analysis_in_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bammey_An_Adaptive_Neural_Network_for_Unsupervised_Mosaic_Consistency_Analysis_in_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bammey_An_Adaptive_Neural_CVPR_2020_supplemental.zip | null | null |
Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation | Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, Richard Bowden | Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performance drastically. In fact, the current state-of-the-art in translation requires gloss level tokenization in order to work. We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation while being trainable in an end-to-end manner. This is achieved by using a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture. This joint approach does not require any ground-truth timing information, simultaneously solving two co-dependant sequence-to-sequence learning problems and leads to significant performance gains. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset. We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models, in some cases more than doubling the performance (9.58 vs. 21.80 BLEU-4 Score). We also share new baseline translation results using transformer networks for several other text-to-text sign language translation tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Camgoz_Sign_Language_Transformers_Joint_End-to-End_Sign_Language_Recognition_and_Translation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13830 | https://www.youtube.com/watch?v=6LH82vP1BhQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Camgoz_Sign_Language_Transformers_Joint_End-to-End_Sign_Language_Recognition_and_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Camgoz_Sign_Language_Transformers_Joint_End-to-End_Sign_Language_Recognition_and_Translation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Context-Aware Loss Function for Action Spotting in Soccer Videos | Anthony Cioppa, Adrien Deliege, Silvio Giancola, Bernard Ghanem, Marc Van Droogenbroeck, Rikke Gade, Thomas B. Moeslund | In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot. We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We show the generalization capability of our loss for generic activity proposals and detection on ActivityNet, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and display challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions and show how such semantic knowledge can be used for automatic highlights generation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cioppa_A_Context-Aware_Loss_Function_for_Action_Spotting_in_Soccer_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01326 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cioppa_A_Context-Aware_Loss_Function_for_Action_Spotting_in_Soccer_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cioppa_A_Context-Aware_Loss_Function_for_Action_Spotting_in_Soccer_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cioppa_A_Context-Aware_Loss_CVPR_2020_supplemental.pdf | null | null |
The Edge of Depth: Explicit Constraints Between Segmentation and Depth | Shengjie Zhu, Garrick Brazil, Xiaoming Liu | In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsupervised monocular depth estimation, constraint from semantic segmentation has been explored implicitly such as sharing and transforming features. In contrast, we propose to explicitly measure the border consistency between segmentation and depth and minimize it in a greedy manner by iteratively supervising the network towards a locally optimal solution. Partially this is motivated by our observation that semantic segmentation even trained with limited ground truth (200 images of KITTI) can offer more accurate border than that of any (monocular or stereo) image-based depth estimation. Through extensive experiments, our proposed approach advance the state of the art on unsupervised monocular depth estimation in the KITTI benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_The_Edge_of_Depth_Explicit_Constraints_Between_Segmentation_and_Depth_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00171 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_The_Edge_of_Depth_Explicit_Constraints_Between_Segmentation_and_Depth_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_The_Edge_of_Depth_Explicit_Constraints_Between_Segmentation_and_Depth_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhu_The_Edge_of_CVPR_2020_supplemental.pdf | null | null |
Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition | Shikai Chen, Jianfeng Wang, Yuedong Chen, Zhongchao Shi, Xin Geng, Yong Rui | Many existing studies reveal that annotation inconsistency widely exists among a variety of facial expression recognition (FER) datasets. The reason might be the subjectivity of human annotators and the ambiguous nature of the expression labels. One promising strategy tackling such a problem is a recently proposed learning paradigm called Label Distribution Learning (LDL), which allows multiple labels with different intensity to be linked to one expression. However, it is often impractical to directly apply label distribution learning because numerous existing datasets only contain one-hot labels rather than label distributions. To solve the problem, we propose a novel approach named Label Distribution Learning on Auxiliary Label Space Graphs(LDL-ALSG) that leverages the topological information of the labels from related but more distinct tasks, such as action unit recognition and facial landmark detection. The underlying assumption is that facial images should have similar expression distributions to their neighbours in the label space of action unit recognition and facial landmark detection. Our proposed method is evaluated on a variety of datasets and outperforms those state-of-the-art methods consistently with a huge margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Label_Distribution_Learning_on_Auxiliary_Label_Space_Graphs_for_Facial_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=T1r2PoN37_M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Label_Distribution_Learning_on_Auxiliary_Label_Space_Graphs_for_Facial_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Label_Distribution_Learning_on_Auxiliary_Label_Space_Graphs_for_Facial_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer | Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, Nenghai Yu | Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. Existing works mainly focus on learning modality-shared representation by embedding different modalities into a same feature space, lowering the upper bound of feature distinctiveness. In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the reidentification performance. We model the affinities of different modality samples according to the shared features and then transfer both shared and specific features among and across modalities. We also propose a complementary feature learning strategy including modality adaption, project adversarial learning and reconstruction enhancement to learn discriminative and complementary shared and specific features of each modality, respectively. The entire cmSSFTalgorithm can be trained in an end-to-end manner. We conducted comprehensive experiments to validate the superiority ofthe overall algorithm and the effectiveness ofeach component. The proposed algorithm significantly outperforms state-of-the-arts by 22.5% and 19.3% mAP on the two mainstream benchmark datasets SYSU-MM01 and RegDB, respectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lu_Cross-Modality_Person_Re-Identification_With_Shared-Specific_Feature_Transfer_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12489 | https://www.youtube.com/watch?v=lrzCzQ6DNHU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_Cross-Modality_Person_Re-Identification_With_Shared-Specific_Feature_Transfer_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_Cross-Modality_Person_Re-Identification_With_Shared-Specific_Feature_Transfer_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning a Unified Sample Weighting Network for Object Detection | Qi Cai, Yingwei Pan, Yu Wang, Jingen Liu, Ting Yao, Tao Mei | Region sampling or weighting is significantly important to the success of modern region-based object detectors. Unlike some previous works, which only focus on "hard" samples when optimizing the objective function, we argue that sample weighting should be data-dependent and task-dependent. The importance of a sample for the objective function optimization is determined by its uncertainties to both object classification and bounding box regression tasks. To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights. Our framework is simple yet effective. It leverages the samples' uncertainty distributions on classification loss, regression loss, IoU, and probability score, to predict sample weights. Our approach has several advantages: (i). It jointly learns sample weights for both classification and regression tasks, which differentiates it from most previous work. (ii). It is a data-driven process, so it avoids some manual parameter tuning. (iii). It can be effortlessly plugged into most object detectors and achieves noticeable performance improvements without affecting their inference time. Our approach has been thoroughly evaluated with recent object detection frameworks and it can consistently boost the detection accuracy. Code has been made available at https://github.com/caiqi/sample-weighting-network. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.06568 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Joint Semantic Segmentation and Boundary Detection Using Iterative Pyramid Contexts | Mingmin Zhen, Jinglu Wang, Lei Zhou, Shiwei Li, Tianwei Shen, Jiaxiang Shang, Tian Fang, Long Quan | In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection. The critical component in the framework is the iterative pyramid context module (PCM), which couples two tasks and stores the shared latent semantics to interact between the two tasks. For semantic boundary detection, we propose the novel spatial gradient fusion to suppress non-semantic edges. As semantic boundary detection is the dual task of semantic segmentation, we introduce a loss function with boundary consistency constraint to improve the boundary pixel accuracy for semantic segmentation. Our extensive experiments demonstrate superior performance over state-of-the-art works, not only in semantic segmentation but also in semantic boundary detection. In particular, a mean IoU score of 81.8% on Cityscapes test set is achieved without using coarse data or any external data for semantic segmentation. For semantic boundary detection, we improve over previous state-of-the-art works by 9.9% in terms of AP and 6.8% in terms of MF(ODS). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhen_Joint_Semantic_Segmentation_and_Boundary_Detection_Using_Iterative_Pyramid_Contexts_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07684 | https://www.youtube.com/watch?v=8OsIloh9-ek | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhen_Joint_Semantic_Segmentation_and_Boundary_Detection_Using_Iterative_Pyramid_Contexts_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhen_Joint_Semantic_Segmentation_and_Boundary_Detection_Using_Iterative_Pyramid_Contexts_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection | Ze Chen, Zhihang Fu, Rongxin Jiang, Yaowu Chen, Xian-Sheng Hua | Based on the framework of multiple instance learning (MIL), tremendous works have promoted the advances of weakly supervised object detection (WSOD). However, most MIL-based methods tend to localize instances to their discriminative parts instead of the whole content. In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations. Specifically, all region proposals in a given image play the role of voters every iteration during training, voting for the likelihood of each category in spatial dimensions. After dilating alignment on the area with large likelihood values, the voting results are regularized as bounding boxes, being used for the final classification and localization. Based on SLV, we further propose an end-to-end training framework for multi-task learning. The classification and localization tasks promote each other, which further improves the detection performance. Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the superior performance of SLV. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_SLV_Spatial_Likelihood_Voting_for_Weakly_Supervised_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.12884 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_SLV_Spatial_Likelihood_Voting_for_Weakly_Supervised_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_SLV_Spatial_Likelihood_Voting_for_Weakly_Supervised_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Robust Superpixel-Guided Attentional Adversarial Attack | Xiaoyi Dong, Jiangfan Han, Dongdong Chen, Jiayang Liu, Huanyu Bian, Zehua Ma, Hongsheng Li, Xiaogang Wang, Weiming Zhang, Nenghai Yu | Deep Neural Networks are vulnerable to adversarial samples, which can fool classifiers by adding small perturbations onto the original image. Since the pioneering optimization-based adversarial attack method, many following methods have been proposed in the past several years. However most of these methods add perturbations in a "pixel-wise" and "global" way. Firstly, because of the contradiction between the local smoothness of natural images and the noisy property of these adversarial perturbations, this "pixel-wise" way makes these methods not robust to image processing based defense methods and steganalysis based detection methods. Secondly, we find adding perturbations to the background is less useful than to the salient object, thus the "global" way is also not optimal. Based on these two considerations, we propose the first robust superpixel-guided attentional adversarial attack method. Specifically, the adversarial perturbations are only added to the salient regions and guaranteed to be same within each superpixel. Through extensive experiments, we demonstrate our method can preserve the attack ability even in this highly constrained modification space. More importantly, compared to existing methods, it is significantly more robust to image processing based defense and steganalysis based detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dong_Robust_Superpixel-Guided_Attentional_CVPR_2020_supplemental.pdf | null | null |
MMTM: Multimodal Transfer Module for CNN Fusion | Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L. Iuzzolino, Kazuhito Koishida | In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end. Due to its simplicity, late fusion is still the predominant approach in many state-of-the-art multimodal applications. In this paper, we present a simple neural network module for leveraging the knowledge from multiple modalities in convolutional neural networks. The proposed unit, named Multimodal Transfer Module (MMTM), can be added at different levels of the feature hierarchy, enabling slow modality fusion. Using squeeze and excitation operations, MMTM utilizes the knowledge of multiple modalities to recalibrate the channel-wise features in each CNN stream. Unlike other intermediate fusion methods, the proposed module could be used for feature modality fusion in convolution layers with different spatial dimensions. Another advantage of the proposed method is that it could be added among unimodal branches with minimum changes in the their network architectures, allowing each branch to be initialized with existing pretrained weights. Experimental results show that our framework improves the recognition accuracy of well-known multimodal networks. We demonstrate state-of-the-art or competitive performance on four datasets that span the task domains of dynamic hand gesture recognition, speech enhancement, and action recognition with RGB and body joints. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Joze_MMTM_Multimodal_Transfer_Module_for_CNN_Fusion_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.08670 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Joze_MMTM_Multimodal_Transfer_Module_for_CNN_Fusion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Joze_MMTM_Multimodal_Transfer_Module_for_CNN_Fusion_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Optical Flow in Dense Foggy Scenes Using Semi-Supervised Learning | Wending Yan, Aashish Sharma, Robby T. Tan | In dense foggy scenes, existing optical flow methods are erroneous. This is due to the degradation caused by dense fog particles that break the optical flow basic assumptions such as brightness and gradient constancy. To address the problem, we introduce a semi-supervised deep learning technique that employs real fog images without optical flow ground-truths in the training process. Our network integrates the domain transformation and optical flow networks in one framework. Initially, given a pair of synthetic fog images, its corresponding clean images and optical flow ground-truths, in one training batch we train our network in a supervised manner. Subsequently, given a pair of real fog images and a pair of clean images that are not corresponding to each other (unpaired), in the next training batch, we train our network in an unsupervised manner. We then alternate the training of synthetic and real data iteratively. We use real data without ground-truths, since to have ground-truths in such conditions is intractable, and also to avoid the overfitting problem of synthetic data training, where the knowledge learned on synthetic data cannot be generalized to real data testing. Together with the network architecture design, we propose a new training strategy that combines supervised synthetic-data training and unsupervised real-data training. Experimental results show that our method is effective and outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_Optical_Flow_in_Dense_Foggy_Scenes_Using_Semi-Supervised_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01905 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Optical_Flow_in_Dense_Foggy_Scenes_Using_Semi-Supervised_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Optical_Flow_in_Dense_Foggy_Scenes_Using_Semi-Supervised_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Memory-Guided Normality for Anomaly Detection | Hyunjong Park, Jongyoun Noh, Bumsub Ham | We address the problem of anomaly detection, that is, detecting anomalous events in a video sequence. Anomaly detection methods based on convolutional neural networks (CNNs) typically leverage proxy tasks, such as reconstructing input video frames, to learn models describing normality without seeing anomalous samples at training time, and quantify the extent of abnormalities using the reconstruction error at test time. The main drawbacks of these approaches are that they do not consider the diversity of normal patterns explicitly, and the powerful representation capacity of CNNs allows to reconstruct abnormal video frames. To address this problem, we present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs. To this end, we propose to use a memory module with a new update scheme where items in the memory record prototypical patterns of normal data. We also present novel feature compactness and separateness losses to train the memory, boosting the discriminative power of both memory items and deeply learned features from normal data. Experimental results on standard benchmarks demonstrate the effectiveness and efficiency of our approach, which outperforms the state of the art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Park_Learning_Memory-Guided_Normality_for_Anomaly_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13228 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_Learning_Memory-Guided_Normality_for_Anomaly_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_Learning_Memory-Guided_Normality_for_Anomaly_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection | Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Yiming Zhang, Kai Xu, Jun Wang | In this paper, we address the 3D object detection task by capturing multi-level contextual information with the self-attention mechanism and multi-scale feature fusion. Most existing 3D object detection methods recognize objects individually, without giving any consideration on contextual information between these objects. Comparatively, we propose Multi-Level Context VoteNet (MLCVNet) to recognize 3D objects correlatively, building on the state-of-the-art VoteNet. We introduce three context modules into the voting and classifying stages of VoteNet to encode contextual information at different levels. Specifically, a Patch-to-Patch Context (PPC) module is employed to capture contextual information between the point patches, before voting for their corresponding object centroid points. Subsequently, an Object-to-Object Context (OOC) module is incorporated before the proposal and classification stage, to capture the contextual information between object candidates. Finally, a Global Scene Context (GSC) module is designed to learn the global scene context. We demonstrate these by capturing contextual information at patch, object and scene levels. Our method is an effective way to promote detection accuracy, achieving new state-of-the-art detection performance on challenging 3D object detection datasets, i.e., SUN RGBD and ScanNet. We also release our code at https://github.com/NUAAXQ/MLCVNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_MLCVNet_Multi-Level_Context_VoteNet_for_3D_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05679 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_MLCVNet_Multi-Level_Context_VoteNet_for_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_MLCVNet_Multi-Level_Context_VoteNet_for_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SQuINTing at VQA Models: Introspecting VQA Models With Sub-Questions | Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh, Eric Horvitz, Marco Tulio Ribeiro, Besmira Nushi, Ece Kamar | Existing VQA datasets contain questions with varying levels of complexity. While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities, a significant portion of questions pose challenges that correspond to reasoning tasks - tasks that can only be answered through a synthesis of perception and knowledge about the world, logic and / or reasoning. Analyzing performance across this distinction allows us to notice when existing VQA models have consistency issues - they answer the reasoning questions correctly but fail on associated low-level perception questions. For example, in Figure 1, models answer the complex reasoning question "Is the banana ripe enough to eat?" correctly, but fail on the associated perception question "Are the bananas mostly green or yellow?" indicating that the model likely answered the reasoning question correctly but for the wrong reason. We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting VQAintrospect, a new dataset1 which currently consists of 200K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split. Our evaluation shows that state-of-the-art VQA models have comparable performance in answering perception and reasoning questions, but suffer from consistency problems. To address this shortcoming, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend to the same parts of the image when answering the reasoning question and the perception sub question. We show that SQuINT improves model consistency by 7%, also marginally improving performance on the Reasoning questions in VQA, while also displaying better attention maps. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Selvaraju_SQuINTing_at_VQA_Models_Introspecting_VQA_Models_With_Sub-Questions_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.06927 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Selvaraju_SQuINTing_at_VQA_Models_Introspecting_VQA_Models_With_Sub-Questions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Selvaraju_SQuINTing_at_VQA_Models_Introspecting_VQA_Models_With_Sub-Questions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
VectorNet: Encoding HD Maps and Agent Dynamics From Vectorized Representation | Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, Cordelia Schmid | Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e.g. pedestrians and vehicles) and road context information (e.g. lanes, traffic lights). This paper introduces VectorNet, a hierarchical graph neural network that first exploits the spatial locality of individual road components represented by vectors and then models the high-order interactions among all components. In contrast to most recent approaches, which render trajectories of moving agents and road context information as bird-eye images and encode them with convolutional neural networks (ConvNets), our approach operates on the primitive vector representation. By operating on the vectorized high definition (HD) maps and agent trajectories, we avoid lossy rendering and computationally intensive ConvNet encoding steps. To further boost VectorNet's capability in learning context features, we propose a novel auxiliary task to recover the randomly masked out map entities and agent trajectories based on their context. We evaluate VectorNet on our in-house behavior prediction benchmark and the recently released Argoverse forecasting dataset. Our method achieves on par or better performance than the competitive rendering approach on both benchmarks while saving over 70% of the model parameters with an order of magnitude reduction in FLOPs. It also obtains state-of-the-art performance on the Argoverse dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_VectorNet_Encoding_HD_Maps_and_Agent_Dynamics_From_Vectorized_Representation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.04259 | https://www.youtube.com/watch?v=fM_exYBSWlA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_VectorNet_Encoding_HD_Maps_and_Agent_Dynamics_From_Vectorized_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_VectorNet_Encoding_HD_Maps_and_Agent_Dynamics_From_Vectorized_Representation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Through Fog High-Resolution Imaging Using Millimeter Wave Radar | Junfeng Guan, Sohrab Madani, Suraj Jog, Saurabh Gupta, Haitham Hassanieh | This paper demonstrates high-resolution imaging using millimeter Wave (mmWave) radars that can function even in dense fog. We leverage the fact that mmWave signals have favorable propagation characteristics in low visibility conditions, unlike optical sensors like cameras and LiDARs which cannot penetrate through dense fog. Millimeter-wave radars, however, suffer from very low resolution, specularity, and noise artifacts. We introduce HawkEye, a system that leverages a cGAN architecture to recover high-frequency shapes from raw low-resolution mmWave heat-maps. We propose a novel design that addresses challenges specific to the structure and nature of the radar signals involved. We also develop a data synthesizer to aid with large-scale dataset generation for training. We implement our system on a custom-built mmWave radar platform and demonstrate performance improvement over both standard mmWave radars and other competitive baselines. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guan_Through_Fog_High-Resolution_Imaging_Using_Millimeter_Wave_Radar_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guan_Through_Fog_High-Resolution_CVPR_2020_supplemental.pdf | null | null |
Self-Supervised Learning of Video-Induced Visual Invariances | Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic | We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI). We consider the implicit hierarchy present in the videos and make use of (i) frame-level invariances (e.g. stability to color and contrast perturbations), (ii) shot/clip-level invariances (e.g. robustness to changes in object orientation and lighting conditions), and (iii) video-level invariances (semantic relationships of scenes across shots/clips), to define a holistic self-supervised loss. Training models using different variants of the proposed framework on videos from the YouTube-8M (YT8M) data set, we obtain state-of-the-art self-supervised transfer learning results on the 19 diverse downstream tasks of the Visual Task Adaptation Benchmark (VTAB), using only 1000 labels per task. We then show how to co-train our models jointly with labeled images, outperforming an ImageNet-pretrained ResNet-50 by 0.8 points with 10x fewer labeled images, as well as the previous best supervised model by 3.7 points using the full ImageNet data set. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tschannen_Self-Supervised_Learning_of_Video-Induced_Visual_Invariances_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02783 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tschannen_Self-Supervised_Learning_of_Video-Induced_Visual_Invariances_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tschannen_Self-Supervised_Learning_of_Video-Induced_Visual_Invariances_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tschannen_Self-Supervised_Learning_of_CVPR_2020_supplemental.pdf | null | null |
Butterfly Transform: An Efficient FFT Based Neural Architecture Design | Keivan Alizadeh vahid, Anish Prabhu, Ali Farhadi, Mohammad Rastegari | In this paper, we show that extending the butterfly operations from the FFT algorithm to a general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for CNN designs. Pointwise convolutions, which we refer to as channel fusions, are the main computational bottleneck in the state-of-the-art efficient CNNs (e.g. MobileNets). We introduce a set of criterion for channel fusion, and prove that BFT yields an asymptotically optimal FLOP count with respect to these criteria. By replacing pointwise convolutions with BFT, we reduce the computational complexity of these layers from O(n^2) to O(n log n) with respect to the number of channels. Our experimental evaluations show that our method results in significant accuracy gains across a wide range of network architectures, especially at low FLOP ranges. For example, BFT results in up to a 6.75% absolute Top-1 improvement for MobileNetV1, 4.4 % for ShuffleNet V2 and 5.4% for MobileNetV3 on ImageNet under a similar number of FLOPS. Notably, ShuffleNet-V2+BFT outperforms state-of-the-art architecture search methods MNasNet, FBNet and MobilenetV3 in the low FLOP regime. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.02256 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/vahid_Butterfly_Transform_An_CVPR_2020_supplemental.pdf | null | null |
Cross-Domain Detection via Graph-Induced Prototype Alignment | Minghao Xu, Hang Wang, Bingbing Ni, Qi Tian, Wenjun Zhang | Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performance. Furthermore, since different instances commonly embody distinct modal information in object detection scenario, the feature alignment of source and target domain is hard to be realized. To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment via elaborate prototype representations. In the nutshell, more precise instance-level features are obtained through graph-based information propagation among region proposals, and, on such basis, the prototype representation of each class is derived for category-level domain alignment. In addition, in order to alleviate the negative effect of class-imbalance on domain adaptation, we design a Class-reweighted Contrastive Loss to harmonize the adaptation training process. Combining with Faster R-CNN, the proposed framework conducts feature alignment in a two-stage manner. Comprehensive results on various cross-domain detection tasks demonstrate that our approach outperforms existing methods with a remarkable margin. Our code is available at https://github.com/ChrisAllenMing/GPA-detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Cross-Domain_Detection_via_Graph-Induced_Prototype_Alignment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12849 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Cross-Domain_Detection_via_Graph-Induced_Prototype_Alignment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Cross-Domain_Detection_via_Graph-Induced_Prototype_Alignment_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_Cross-Domain_Detection_via_CVPR_2020_supplemental.pdf | null | null |
What Makes Training Multi-Modal Classification Networks Hard? | Weiyao Wang, Du Tran, Matt Feiszli | Consider end-to-end training of a multi-modal vs. a uni-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its uni-modal counterpart. In our experiments, however, we observe the opposite: the best uni-modal network can outperform the multi-modal network. This observation is consistent across different combinations of modalities and on different tasks and benchmarks for video classifications. This paper identifies two main causes for this performance drop: first, multi-modal networks are often prone to overfitting due to increased capacity. Second, different modalities overfit and generalize at different rates, so training them jointly with a single optimization strategy is sub-optimal. We address these two problems with a technique we call Gradient-Blending, which computes an optimal blending of modalities based on their overfitting behaviors. We demonstrate that Gradient Blending outperforms widely-used baselines for avoiding overfitting and achieves state-of-the-art accuracy on various tasks including human action recognition, ego-centric action recognition, and acoustic event detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_What_Makes_Training_Multi-Modal_Classification_Networks_Hard_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.12681 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_What_Makes_Training_Multi-Modal_Classification_Networks_Hard_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_What_Makes_Training_Multi-Modal_Classification_Networks_Hard_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_What_Makes_Training_CVPR_2020_supplemental.pdf | null | null |
Sparse Layered Graphs for Multi-Object Segmentation | Niels Jeppesen, Anders N. Christensen, Vedrana A. Dahl, Anders B. Dahl | We introduce the novel concept of a Sparse Layered Graph (SLG) for s-t graph cut segmentation of image data. The concept is based on the widely used Ishikawa layered technique for multi-object segmentation, which allows explicit object interactions, such as containment and exclusion with margins. However, the spatial complexity of the Ishikawa technique limits its use for many segmentation problems. To solve this issue, we formulate a general method for adding containment and exclusion interaction constraints to layered graphs. Given some prior knowledge, we can create a SLG, which is often orders of magnitude smaller than traditional Ishikawa graphs, with identical segmentation results. This allows us to solve many problems that could previously not be solved using general graph cut algorithms. We then propose three algorithms for further reducing the spatial complexity of SLGs, by using ordered multi-column graphs. In our experiments, we show that SLGs, and in particular ordered multi-column SLGs, can produce high-quality segmentation results using extremely simple data terms. We also show the scalability of ordered multi-column SLGs, by segmenting a high-resolution volume with several hundred interacting objects. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jeppesen_Sparse_Layered_Graphs_for_Multi-Object_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jeppesen_Sparse_Layered_Graphs_for_Multi-Object_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jeppesen_Sparse_Layered_Graphs_for_Multi-Object_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jeppesen_Sparse_Layered_Graphs_CVPR_2020_supplemental.zip | null | null |
Few-Shot Class-Incremental Learning | Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, Yihong Gong | The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tao_Few-Shot_Class-Incremental_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.10956 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tao_Few-Shot_Class-Incremental_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tao_Few-Shot_Class-Incremental_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tao_Few-Shot_Class-Incremental_Learning_CVPR_2020_supplemental.pdf | null | null |
Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection | Zhonghua Wu, Qingyi Tao, Guosheng Lin, Jianfei Cai | Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations. Our proposed method combines bottom-up and top-down cues for novel class detection. Within our approach, we introduce a bottom-up mechanism based on the well-trained fully supervised object detector (i.e. Faster RCNN) as an object region estimator for web images by recognizing the common objectiveness shared by base and novel classes. With the estimated regions on the web images, we then utilize the top-down attention cues as the guidance for region classification. Furthermore, we propose a residual feature refinement (RFR) block to tackle the domain mismatch between web domain and the target domain. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits. Without any target-domain novel-class images and annotations, our proposed webly supervised object detection model is able to achieve promising performance for novel classes. Moreover, we also conduct transfer learning experiments on large scale ILSVRC 2013 detection dataset and achieve state-of-the-art performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Exploring_Bottom-Up_and_Top-Down_Cues_With_Attentive_Learning_for_Webly_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09790 | https://www.youtube.com/watch?v=czG3FVcU8pM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Exploring_Bottom-Up_and_Top-Down_Cues_With_Attentive_Learning_for_Webly_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Exploring_Bottom-Up_and_Top-Down_Cues_With_Attentive_Learning_for_Webly_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Exploring_Bottom-Up_and_CVPR_2020_supplemental.pdf | null | null |
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization | Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song | Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Du_SpineNet_Learning_Scale-Permuted_Backbone_for_Recognition_and_Localization_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.05027 | https://www.youtube.com/watch?v=add8MZMFTF8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_SpineNet_Learning_Scale-Permuted_Backbone_for_Recognition_and_Localization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_SpineNet_Learning_Scale-Permuted_Backbone_for_Recognition_and_Localization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation | Keunhong Park, Arsalan Mousavian, Yu Xiang, Dieter Fox | Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a result, they are difficult to scale to a large number of objects and cannot be directly applied to unseen objects. We propose a novel framework for 6D pose estimation of unseen objects. We present a network that reconstructs a latent 3D representation of an object using a small number of reference views at inference time. Our network is able to render the latent 3D representation from arbitrary views. Using this neural renderer, we directly optimize for pose given an input image. By training our network with a large number of 3D shapes for reconstruction and rendering, our network generalizes well to unseen objects. We present a new dataset for unseen object pose estimation--MOPED. We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet and LINEMOD datasets. Our method performs competitively to supervised methods that are trained on those objects. Code and data will be available at https://keunhong.com/publications/latentfusion/ | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Park_LatentFusion_End-to-End_Differentiable_Reconstruction_and_Rendering_for_Unseen_Object_Pose_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00416 | https://www.youtube.com/watch?v=ajrF8L1Metg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_LatentFusion_End-to-End_Differentiable_Reconstruction_and_Rendering_for_Unseen_Object_Pose_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Park_LatentFusion_End-to-End_Differentiable_Reconstruction_and_Rendering_for_Unseen_Object_Pose_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Offset Bin Classification Network for Accurate Object Detection | Heqian Qiu, Hongliang Li, Qingbo Wu, Hengcan Shi | Object detection combines object classification and object localization problems. Most existing object detection methods usually locate objects by leveraging regression networks trained with Smooth L1 loss function to predict offsets between candidate boxes and objects. However, this loss function applies the same penalties on different samples with large errors, which results in suboptimal regression networks and inaccurate offsets. In this paper, we propose an offset bin classification network optimized with cross entropy loss to predict more accurate offsets. It not only provides different penalties for different samples but also avoids the gradient explosion problem caused by the samples with large errors. Specifically, we discretize the continuous offset into a number of bins, and predict the probability of each offset bin. Furthermore, we propose an expectation-based offset prediction and a hierarchical focusing method to improve the prediction precision. Extensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate the effectiveness of our proposed method. Our method outperforms the baseline methods by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qiu_Offset_Bin_Classification_Network_for_Accurate_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiu_Offset_Bin_Classification_Network_for_Accurate_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiu_Offset_Bin_Classification_Network_for_Accurate_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations | Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh, Sathya N. Ravi, Vikas Singh | Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision. Recent theoretical results suggest that despite their excellent practical performance, in various cases, a substitution with basis expansions (e.g., polynomials) can yield significant benefits from both the optimization and generalization perspective. Unfortunately, the existing results remain limited to networks with a couple of layers, and the practical viability of these results is not yet known. Motivated by some of these results, we explore the use of Hermite polynomial expansions as a substitute for ReLUs in deep networks. While our experiments with supervised learning do not provide a clear verdict, we find that this strategy offers considerable benefits in semi-supervised learning (SSL) / transductive learning settings. We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings (due to concurrent runtime benefits). Further, we show via theoretical analysis, that the networks (with Hermite activations) offer robustness to noise and other attractive mathematical properties. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lokhande_Generating_Accurate_Pseudo-Labels_in_Semi-Supervised_Learning_and_Avoiding_Overconfident_Predictions_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.05479 | https://www.youtube.com/watch?v=FeyR1tanlgI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lokhande_Generating_Accurate_Pseudo-Labels_in_Semi-Supervised_Learning_and_Avoiding_Overconfident_Predictions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lokhande_Generating_Accurate_Pseudo-Labels_in_Semi-Supervised_Learning_and_Avoiding_Overconfident_Predictions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lokhande_Generating_Accurate_Pseudo-Labels_CVPR_2020_supplemental.zip | null | null |
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation | Chaoyang He, Haishan Ye, Li Shen, Tong Zhang | Many recently proposed methods for Neural Architecture Search (NAS) can be formulated as bilevel optimization. For efficient implementation, its solution requires approximations of second-order methods. In this paper, we demonstrate that gradient errors caused by such approximations lead to suboptimality, in the sense that the optimization procedure fails to converge to a (locally) optimal solution. To remedy this, this paper proposes MiLeNAS, a mixed-level reformulation for NAS that can be optimized efficiently and reliably. It is shown that even when using a simple first-order method on the mixed-level formulation, MiLeNAS can achieve a lower validation error for NAS problems. Consequently, architectures obtained by our method achieve consistently higher accuracies than those obtained from bilevel optimization. Moreover, MiLeNAS proposes a framework beyond DARTS. It is upgraded via model size-based search and early stopping strategies to complete the search process in around 5 hours. Extensive experiments within the convolutional architecture search space validate the effectiveness of our approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/He_MiLeNAS_Efficient_Neural_Architecture_Search_via_Mixed-Level_Reformulation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12238 | https://www.youtube.com/watch?v=wTuAx2Fd4q0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/He_MiLeNAS_Efficient_Neural_Architecture_Search_via_Mixed-Level_Reformulation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/He_MiLeNAS_Efficient_Neural_Architecture_Search_via_Mixed-Level_Reformulation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/He_MiLeNAS_Efficient_Neural_CVPR_2020_supplemental.pdf | null | null |
G-TAD: Sub-Graph Localization for Temporal Action Detection | Mengmeng Xu, Chen Zhao, David S. Rojas, Ali Thabet, Bernard Ghanem | Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design an SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActivityNet-1.3 it obtains an average mAP of 34.09%; on THUMOS14 it reaches 51.6% at [email protected] when combined with a proposal processing method. The code has been made available at https://github.com/frostinassiky/gtad. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_G-TAD_Sub-Graph_Localization_for_Temporal_Action_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_G-TAD_Sub-Graph_Localization_for_Temporal_Action_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_G-TAD_Sub-Graph_Localization_for_Temporal_Action_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_G-TAD_Sub-Graph_Localization_CVPR_2020_supplemental.pdf | null | null |
Learning Saliency Propagation for Semi-Supervised Instance Segmentation | Yanzhao Zhou, Xin Wang, Jianbin Jiao, Trevor Darrell, Fisher Yu | Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. We aim to improve the accuracy of the existing instance segmentation models by utilizing a large amount of detection supervision. We propose ShapeProp, which learns to activate the salient regions within the object detection and propagate the areas to the whole instance through an iterative learnable message passing module. ShapeProp can benefit from more bounding box supervision to locate the instances more accurately and utilize the feature activations from the larger number of instances to achieve more accurate segmentation. We extensively evaluate ShapeProp on three datasets (MS COCO, PASCAL VOC, and BDD100k) with different supervision setups based on both two-stage (Mask R-CNN) and single-stage (RetinaMask) models. The results show our method establishes new states of the art for semi-supervised instance segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Dataless Model Selection With the Deep Frame Potential | Calvin Murdock, Simon Lucey | Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency. Standard approaches rely on ad-hoc engineering or computationally expensive validation on a specific dataset. We instead attempt to quantify networks by their intrinsic capacity for unique and robust representations, enabling efficient architecture comparisons without requiring any data. Building upon theoretical connections between deep learning and sparse approximation, we propose the deep frame potential: a measure of coherence that is approximately related to representation stability but has minimizers that depend only on network structure. This provides a framework for jointly quantifying the contributions of architectural hyper-parameters such as depth, width, and skip connections. We validate its use as a criterion for model selection and demonstrate correlation with generalization error on a variety of common residual and densely connected network architectures. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Murdock_Dataless_Model_Selection_With_the_Deep_Frame_Potential_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13866 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Murdock_Dataless_Model_Selection_With_the_Deep_Frame_Potential_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Murdock_Dataless_Model_Selection_With_the_Deep_Frame_Potential_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
MUXConv: Information Multiplexing in Convolutional Neural Networks | Zhichao Lu, Kalyanmoy Deb, Vishnu Naresh Boddeti | Convolutional neural networks have witnessed remarkable improvements in computational efficiency in recent years. A key driving force has been the idea of trading-off model expressivity and efficiency through a combination of 1x1 and depth-wise separable convolutions in lieu of a standard convolutional layer. The price of the efficiency, however, is the sub-optimal flow of information across space and channels in the network. To overcome this limitation, we present MUXConv, a layer that is designed to increase the flow of information by progressively multiplexing channel and spatial information in the network, while mitigating computational complexity. Furthermore, to demonstrate the effectiveness of MUXConv, we integrate it within an efficient multi-objective evolutionary algorithm to search for the optimal model hyper-parameters while simultaneously optimizing accuracy, compactness, and computational efficiency. On ImageNet, the resulting models, dubbed MUXNets, match the performance (75.3% top-1 accuracy) and multiply-add operations (218M) of MobileNetV3 while being 1.6x more compact, and outperform other mobile models in all the three criteria. MUXNet also performs well under transfer learning and when adapted to object detection. On the ChestX-Ray 14 benchmark, its accuracy is comparable to the state-of-the-art while being 3.3x more compact and 14x more efficient. Similarly, detection on PASCAL VOC 2007 is 1.2% more accurate, 28% faster and 6% more compact compared to MobileNetV2. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13880 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lu_MUXConv_Information_Multiplexing_CVPR_2020_supplemental.pdf | null | null |
Learning to Segment 3D Point Clouds in 2D Image Space | Yecheng Lyu, Xinming Huang, Ziming Zhang | In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation. To this end, we are motivated by graph drawing and reformulate it as an integer programming problem to learn the topology-preserving graph-to-grid mapping for each individual point cloud. To accelerate the computation in practice, we further propose a novel hierarchical approximate algorithm. With the help of the Delaunay triangulation for graph construction from point clouds and a multi-scale U-Net for segmentation, we manage to demonstrate the state-of-the-art performance on ShapeNet and PartNet, respectively, with significant improvement over the literature. Code is available at https://github.com/Zhang-VISLab. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lyu_Learning_to_Segment_3D_Point_Clouds_in_2D_Image_Space_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05593 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lyu_Learning_to_Segment_3D_Point_Clouds_in_2D_Image_Space_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lyu_Learning_to_Segment_3D_Point_Clouds_in_2D_Image_Space_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Interactive Image Segmentation With First Click Attention | Zheng Lin, Zhao Zhang, Lin-Zhuo Chen, Ming-Ming Cheng, Shao-Ping Lu | In the task of interactive image segmentation, users initially click one point to segment the main body of the target object and then provide more points on mislabeled regions iteratively for a precise segmentation. Existing methods treat all interaction points indiscriminately, ignoring the difference between the first click and the remaining ones. In this paper, we demonstrate the critical role of the first click about providing the location and main body information of the target object. A deep framework, named First Click Attention Network (FCA-Net), is proposed to make better use of the first click. In this network, the interactive segmentation result can be much improved with the following benefits: focus invariance, location guidance, and error-tolerant ability. We then put forward a click-based loss function and a structural integrity strategy for better segmentation effect. The visualized segmentation results and sufficient experiments on five datasets demonstrate the importance of the first click and the superiority of our FCA-Net. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Interactive_Image_Segmentation_With_First_Click_Attention_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Interactive_Image_Segmentation_With_First_Click_Attention_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Interactive_Image_Segmentation_With_First_Click_Attention_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization | Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, Feiyue Huang | Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, illumination, etc. An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC. Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree. The final decision is computed as the summation of the predictions from leaf nodes. The deep convolutional operations learn to capture the representations of objects, and the tree structure characterizes the coarse-to-fine hierarchical feature learning process. In addition, we use the attention transformer module to enforce the network to capture discriminative features. The negative log-likelihood loss is used to train the entire network in an end-to-end fashion by SGD with back-propagation. Several experiments on the CUB-200-2011, Stanford Cars and Aircraft datasets demonstrate that the proposed method performs favorably against the state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.11378 | https://www.youtube.com/watch?v=PC3s2U_MehQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Dynamic Convolution: Attention Over Convolution Kernels | Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, Zicheng Liu | Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability. To address this issue, we present Dynamic Convolution, a new design that increases model complexity without increasing the network depth or width. Instead of using a single convolution kernel per layer, dynamic convolution aggregates multiple parallel convolution kernels dynamically based upon their attentions, which are input dependent. Assembling multiple kernels is not only computationally efficient due to the small kernel size, but also has more representation power since these kernels are aggregated in a non-linear way via attention. By simply using dynamic convolution for the state-of-the-art architecture MobileNetV3-Small, the top-1 accuracy of ImageNet classification is boosted by 2.9% with only 4% additional FLOPs and 2.9 AP gain is achieved on COCO keypoint detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Dynamic_Convolution_Attention_Over_Convolution_Kernels_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03458 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Dynamic_Convolution_Attention_Over_Convolution_Kernels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Dynamic_Convolution_Attention_Over_Convolution_Kernels_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Transform and Tell: Entity-Aware News Image Captioning | Alasdair Tran, Alexander Mathews, Lexing Xie | We propose an end-to-end model which generates captions for images embedded in news articles. News images present two key challenges: they rely on real-world knowledge, especially about named entities; and they typically have linguistically rich captions that include uncommon words. We address the first challenge by associating words in the caption with faces and objects in the image, via a multi-modal, multi-head attention mechanism. We tackle the second challenge with a state-of-the-art transformer language model that uses byte-pair-encoding to generate captions as a sequence of word parts. On the GoodNews dataset, our model outperforms the previous state of the art by a factor of four in CIDEr score (13 to 54). This performance gain comes from a unique combination of language models, word representation, image embeddings, face embeddings, object embeddings, and improvements in neural network design. We also introduce the NYTimes800k dataset which is 70% larger than GoodNews, has higher article quality, and includes the locations of images within articles as an additional contextual cue. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tran_Transform_and_Tell_Entity-Aware_News_Image_Captioning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08070 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tran_Transform_and_Tell_Entity-Aware_News_Image_Captioning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tran_Transform_and_Tell_Entity-Aware_News_Image_Captioning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tran_Transform_and_Tell_CVPR_2020_supplemental.pdf | null | null |
MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning | Yuan Gao, Haoping Bai, Zequn Jie, Jiayi Ma, Kui Jia, Wei Liu | We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL). Existing NAS methods typically define different search spaces according to different tasks. In order to adapt to different task combinations (i.e., task sets), we disentangle the GP-MTL networks into single-task backbones (optionally encode the task priors), and a hierarchical and layerwise features sharing/fusing scheme across them. This enables us to design a novel and general task-agnostic search space, which inserts cross-task edges (i.e., feature fusion connections) into fixed single-task network backbones. Moreover, we also propose a novel single-shot gradient-based search algorithm that closes the performance gap between the searched architectures and the final evaluation architecture. This is realized with a minimum entropy regularization on the architecture weights during the search phase, which makes the architecture weights converge to near-discrete values and therefore achieves a single model. As a result, our searched model can be directly used for evaluation without (re-)training from scratch. We perform extensive experiments using different single-task backbones on various task sets, demonstrating the promising performance obtained by exploiting the hierarchical and layerwise features, as well as the desirable generalizability to different i) task sets and ii) single-task backbones. The code of our paper is available at https://github.com/bhpfelix/MTLNAS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_MTL-NAS_Task-Agnostic_Neural_Architecture_Search_Towards_General-Purpose_Multi-Task_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=jyDCrdlQX8A | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_MTL-NAS_Task-Agnostic_Neural_Architecture_Search_Towards_General-Purpose_Multi-Task_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_MTL-NAS_Task-Agnostic_Neural_Architecture_Search_Towards_General-Purpose_Multi-Task_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_MTL-NAS_Task-Agnostic_Neural_CVPR_2020_supplemental.pdf | null | null |
12-in-1: Multi-Task Vision and Language Representation Learning | Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee | Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task model. Our approach culminates in a single model on 12 datasets from four broad categories of task including visual question answering, caption-based image retrieval, grounding referring expressions, and multimodal verification. Compared to independently trained single-task models, this represents a reduction from approximately 3 billion parameters to 270 million while simultaneously improving performance by 2.05 points on average across tasks. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Further, we show that finetuning task-specific models from our single multi-task model can lead to further improvements, achieving performance at or above the state-of-the-art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lu_12-in-1_Multi-Task_Vision_and_Language_Representation_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_12-in-1_Multi-Task_Vision_and_Language_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_12-in-1_Multi-Task_Vision_and_Language_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lu_12-in-1_Multi-Task_Vision_CVPR_2020_supplemental.pdf | null | null |
Disentangling Physical Dynamics From Unknown Factors for Unsupervised Video Prediction | Vincent Le Guen, Nicolas Thome | Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video forecasting models. Since physics is too restrictive for describing the full visual content of generic video sequences, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information. A second contribution is to propose a new recurrent physical cell (PhyCell), inspired from data assimilation techniques, for performing PDE-constrained prediction in latent space. Extensive experiments conducted on four various datasets show the ability of PhyDNet to outperform state-of-the-art methods. Ablation studies also highlight the important gain brought out by both disentanglement and PDE-constrained prediction. Finally, we show that PhyDNet presents interesting features for dealing with missing data and long-term forecasting. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Le_Guen_Disentangling_Physical_Dynamics_From_Unknown_Factors_for_Unsupervised_Video_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.01460 | https://www.youtube.com/watch?v=_edOGTNSC1U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Le_Guen_Disentangling_Physical_Dynamics_From_Unknown_Factors_for_Unsupervised_Video_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Le_Guen_Disentangling_Physical_Dynamics_From_Unknown_Factors_for_Unsupervised_Video_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Le_Guen_Disentangling_Physical_Dynamics_CVPR_2020_supplemental.pdf | null | null |
Gold Seeker: Information Gain From Policy Distributions for Goal-Oriented Vision-and-Langauge Reasoning | Ehsan Abbasnejad, Iman Abbasnejad, Qi Wu, Javen Shi, Anton van den Hengel | As Computer Vision moves from passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover it. We propose a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to this distribution. For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed. The action taken is that which maximizes the potential information gain. We demonstrate this approach applied to two vision-and-language problems that have attracted significant recent interest, visual dialog and visual query generation. In both cases the method actively selects actions that will best reduce its internal uncertainty, and outperforms its competitors in achieving the goal of the challenge. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Abbasnejad_Gold_Seeker_Information_Gain_From_Policy_Distributions_for_Goal-Oriented_Vision-and-Langauge_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.06398 | https://www.youtube.com/watch?v=hVwKN7TlJF8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Abbasnejad_Gold_Seeker_Information_Gain_From_Policy_Distributions_for_Goal-Oriented_Vision-and-Langauge_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Abbasnejad_Gold_Seeker_Information_Gain_From_Policy_Distributions_for_Goal-Oriented_Vision-and-Langauge_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context | Chenchen Liu, Yang Jin, Kehan Xu, Guoqiang Gong, Yadong Mu | Video visual relation detection (VidVRD) aims to describe all interacting objects in a video. Different from relationships in static images, videos contain an addition temporal channel. A majority of existing works divide a video into short segments, predict relationships in each segment, and merge them. Such methods cannot capture relations involving long motions. Predicting the same relationship across neighboring video segments is also inefficient. To address these issues, this work proposes a novel sliding-window scheme to simultaneously predict short-term and long-term relationships. We run windows with different kernel sizes on object tracklets to generate sub-tracklet proposals with different duration, while the computational load is similar to that in segment-based methods. To fully utilize spatial and temporal information in videos, we construct one spatial and one temporal graph and employ Graph Convloutional Network to generate contextual embedding for tracklet proposal compatibility evaluation. We only predict relationships on highly-compatible proposal pairs. Our method achieves state-of-the-art performance on both ImageNet-VidVRD and VidOR dataset across multiple tasks. Especially for ImageNet-VidVRD, we obtain an average of 3% (R@50 from 8.07% to 11.21%) improvement under all evaluation metrics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Beyond_Short-Term_Snippet_Video_Relation_Detection_With_Spatio-Temporal_Global_Context_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=BA5ru9P83Yo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Beyond_Short-Term_Snippet_Video_Relation_Detection_With_Spatio-Temporal_Global_Context_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Beyond_Short-Term_Snippet_Video_Relation_Detection_With_Spatio-Temporal_Global_Context_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Semi-Supervised Semantic Image Segmentation With Self-Correcting Networks | Mostafa S. Ibrahim, Arash Vahdat, Mani Ranjbar, William G. Macready | Building a large image dataset with high-quality object masks for semantic segmentation is costly and time-consuming. In this paper, we introduce a principled semi-supervised framework that only use a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak-set). Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weak-set and a self-correction module that improves the generated labels during training using the increasingly accurate primary model. We introduce two variants of the self-correction module using either linear or convolutional functions. Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models trained with a small fully supervised set perform similar to, or better than, models trained with a large fully supervised set while requiring 7x less annotation effort. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ibrahim_Semi-Supervised_Semantic_Image_Segmentation_With_Self-Correcting_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1811.07073 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ibrahim_Semi-Supervised_Semantic_Image_Segmentation_With_Self-Correcting_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ibrahim_Semi-Supervised_Semantic_Image_Segmentation_With_Self-Correcting_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition | Boyan Zhou, Quan Cui, Xiu-Shen Wei, Zhao-Min Chen | Our work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme imbalance for dealing with long-tailed problems. In this paper, we firstly discover that these re-balancing methods achieving satisfactory recognition accuracy owe to that they could significantly promote the classifier learning of deep networks. However, at the same time, they will unexpectedly damage the representative ability of the learned deep features to some extent. Therefore, we propose a unified Bilateral-Branch Network (BBN) to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately. In particular, our BBN model is further equipped with a novel cumulative learning strategy, which is designed to first learn the universal patterns and then pay attention to the tail data gradually. Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods. Furthermore, validation experiments can demonstrate both our preliminary discovery and effectiveness of tailored designs in BBN for long-tailed problems. Our method won the first place in the iNaturalist 2019 large scale species classification competition, and our code is open-source and available at https://github.com/Megvii-Nanjing/BBN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_BBN_Bilateral-Branch_Network_With_Cumulative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02413 | https://www.youtube.com/watch?v=VU05QyLF0-I | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_BBN_Bilateral-Branch_Network_With_Cumulative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_BBN_Bilateral-Branch_Network_With_Cumulative_Learning_for_Long-Tailed_Visual_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_BBN_Bilateral-Branch_Network_CVPR_2020_supplemental.pdf | null | null |
Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval | Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song | Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is however hindered by the fact that drawing a sketch takes time, and most people struggle to draw a complete and faithful sketch. In this paper, we reformulate the conventional FG-SBIR framework to tackle these challenges, with the ultimate goal of retrieving the target photo with the least number of strokes possible. We further propose an on-the-fly design that starts retrieving as soon as the user starts drawing. To accomplish this, we devise a reinforcement learning based cross-modal retrieval framework that directly optimizes rank of the ground-truth photo over a complete sketch drawing episode. Additionally, we introduce a novel reward scheme that circumvents the problems related to irrelevant sketch strokes, and thus provides us with a more consistent rank list during the retrieval. We achieve superior early-retrieval efficiency over state-of-the-art methods and alternative baselines on two publicly available fine-grained sketch retrieval datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bhunia_Sketch_Less_for_More_On-the-Fly_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10310 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhunia_Sketch_Less_for_More_On-the-Fly_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bhunia_Sketch_Less_for_More_On-the-Fly_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction | Zhishuai Zhang, Jiyang Gao, Junhua Mao, Yukai Liu, Dragomir Anguelov, Congcong Li | Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detection and prediction as separate tasks or simply add a trajectory regression head on top of a detector. In this work, we present a novel end-to-end two-stage network: Spatio-Temporal-Interactive Network (STINet). In addition to 3D geometry modeling of pedestrians, we model the temporal information for each of the pedestrians. To do so, our method predicts both current and past locations in the first stage, so that each pedestrian can be linked across frames and the comprehensive spatio-temporal information can be captured in the second stage. Also, we model the interaction among objects with an interaction graph, to gather the information among the neighboring objects. Comprehensive experiments on the Lyft Dataset and the recently released large-scale Waymo Open Dataset for both object detection and future trajectory prediction validate the effectiveness of the proposed method. For the Waymo Open Dataset, we achieve a bird-eyes-view (BEV) detection AP of 80.73 and trajectory prediction average displacement error (ADE) of 33.67cm for pedestrians, which establish the state-of-the-art for both tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_STINet_Spatio-Temporal-Interactive_Network_for_Pedestrian_Detection_and_Trajectory_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.04255 | https://www.youtube.com/watch?v=hHWgunSDTNM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_STINet_Spatio-Temporal-Interactive_Network_for_Pedestrian_Detection_and_Trajectory_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_STINet_Spatio-Temporal-Interactive_Network_for_Pedestrian_Detection_and_Trajectory_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_STINet_Spatio-Temporal-Interactive_Network_CVPR_2020_supplemental.pdf | null | null |
Intelligent Home 3D: Automatic 3D-House Design From Linguistic Descriptions Only | Qi Chen, Qi Wu, Rui Tang, Yuhan Wang, Shuai Wang, Mingkui Tan | Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language. In this paper, we formulate it as a language conditioned visual content generation problem that is further divided into a floor plan generation and an interior texture (such as floor and wall) synthesis task. The only control signal of the generation process is the linguistic expression given by users that describe the house details. To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC-LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN). With some post-processing, the final product of this task is a 3D house model. To train and evaluate our model, we build the first Text--to--3D House Model dataset, which will be released at: https:// hidden-link-for-submission. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Intelligent_Home_3D_Automatic_3D-House_Design_From_Linguistic_Descriptions_Only_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00397 | https://www.youtube.com/watch?v=TJtPP8WFhgw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Intelligent_Home_3D_Automatic_3D-House_Design_From_Linguistic_Descriptions_Only_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Intelligent_Home_3D_Automatic_3D-House_Design_From_Linguistic_Descriptions_Only_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Intelligent_Home_3D_CVPR_2020_supplemental.pdf | null | null |
Mask Encoding for Single Shot Instance Segmentation | Rufeng Zhang, Zhi Tian, Chunhua Shen, Mingyu You, Youliang Yan | To date, instance segmentation is dominated by two-stage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple single-shot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: git.io/AdelaiDet | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Mask_Encoding_for_Single_Shot_Instance_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11712 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Mask_Encoding_for_Single_Shot_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Mask_Encoding_for_Single_Shot_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection | Zhiwei Dong, Guoxuan Li, Yue Liao, Fei Wang, Pengju Ren, Chen Qian | Keypoint-based detectors have achieved pretty-well performance. However, incorrect keypoint matching is still widespread and greatly affects the performance of the detector. In this paper, we propose CentripetalNet which uses centripetal shift to pair corner keypoints from the same instance. CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned. Combining position information, our approach matches corner points more accurately than the conventional embedding approaches do. Corner pooling extracts information inside the bounding boxes onto the border. To make this information more aware at the corners, we design a cross-star deformable convolution network to conduct feature adaption. Furthermore, we explore instance segmentation on anchor-free detectors by equipping our CentripetalNet with a mask prediction module. On COCO test-dev, our CentripetalNet not only outperforms all existing anchor-free detectors with an AP of 48.0% but also achieves comparable performance to the state-of-the-art instance segmentation approaches with a 40.2% Mask AP. Code is available at https: //github.com/KiveeDong/CentripetalNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_CentripetalNet_Pursuing_High-Quality_Keypoint_Pairs_for_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09119 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_CentripetalNet_Pursuing_High-Quality_Keypoint_Pairs_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_CentripetalNet_Pursuing_High-Quality_Keypoint_Pairs_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hierarchical Feature Embedding for Attribute Recognition | Jie Yang, Jiarou Fan, Yiru Wang, Yige Wang, Weihao Gan, Lin Liu, Wei Wu | Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc. Most of previous work only consider the attribute-level feature embedding, which might perform poorly in complicated heterogeneous conditions. To address this problem, we propose a hierarchical feature embedding (HFE) framework, which learns a fine-grained feature embedding by combining attribute and ID information. In HFE, we maintain the inter-class and intra-class feature embedding simultaneously. Not only samples with the same attribute but also samples with the same ID are gathered more closely, which could restrict the feature embedding of visually hard samples with regard to attributes and improve the robustness to variant conditions. We establish this hierarchical structure by utilizing HFE loss consisted of attribute-level and ID-level constraints. We also introduce an absolute boundary regularization and a dynamic loss weight as supplementary components to help build up the feature embedding. Experiments show that our method achieves the state-of-the-art results on two pedestrian attribute datasets and a facial attribute dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Hierarchical_Feature_Embedding_for_Attribute_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.11576 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Hierarchical_Feature_Embedding_for_Attribute_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Hierarchical_Feature_Embedding_for_Attribute_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Mixture Dense Regression for Object Detection and Human Pose Estimation | Ali Varamesh, Tinne Tuytelaars | Mixture models are well-established learning approaches that, in computer vision, have mostly been applied to inverse or ill-defined problems. However, they are general-purpose divide-and-conquer techniques, splitting the input space into relatively homogeneous subsets in a data-driven manner. Not only ill-defined but also well-defined complex problems should benefit from them. To this end, we devise a framework for spatial regression using mixture density networks. We realize the framework for object detection and human pose estimation. For both tasks, a mixture model yields higher accuracy and divides the input space into interpretable modes. For object detection, mixture components focus on object scale, with the distribution of components closely following that of ground truth the object scale. This practically alleviates the need for multi-scale testing, providing a superior speed-accuracy trade-off. For human pose estimation, a mixture model divides the data based on viewpoint and uncertainty -- namely, front and back views, with back view imposing higher uncertainty. We conduct experiments on the MS COCO dataset and do not face any mode collapse. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Varamesh_Mixture_Dense_Regression_for_Object_Detection_and_Human_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00821 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Varamesh_Mixture_Dense_Regression_for_Object_Detection_and_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Varamesh_Mixture_Dense_Regression_for_Object_Detection_and_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Varamesh_Mixture_Dense_Regression_CVPR_2020_supplemental.pdf | null | null |
Don't Even Look Once: Synthesizing Features for Zero-Shot Detection | Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama | Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable. While vanilla deep neural networks deliver high performance for objects available during training, unseen object detection degrades significantly. At a fundamental level, while vanilla detectors are capable of proposing bounding boxes, which include unseen objects, they are often incapable of assigning high-confidence to unseen objects, due to the inherent precision/recall tradeoffs that requires rejecting background objects. We propose a novel detection algorithm "Don't Even Look Once (DELO)," that synthesizes visual features for unseen objects and augments existing training algorithms to incorporate unseen object detection. Our proposed scheme is evaluated on Pascal VOC and MSCOCO, and we demonstrate significant improvements in test accuracy over vanilla and other state-of-art zero-shot detectors | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Dont_Even_Look_Once_Synthesizing_Features_for_Zero-Shot_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Dont_Even_Look_Once_Synthesizing_Features_for_Zero-Shot_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Dont_Even_Look_Once_Synthesizing_Features_for_Zero-Shot_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Detection in Crowded Scenes: One Proposal, Multiple Predictions | Xuangeng Chu, Anlin Zheng, Xiangyu Zhang, Jian Sun | We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes. The key of our approach is to let each proposal predict a set of correlated instances rather than a single one in previous proposal-based frameworks. Equipped with new techniques such as EMD Loss and Set NMS, our detector can effectively handle the difficulty of detecting highly overlapped objects. On a FPN-Res50 baseline, our detector can obtain 4.9% AP gains on challenging CrowdHuman dataset and 1.0% \text MR ^ -2 improvements on CityPersons dataset, without bells and whistles. Moreover, on less crowed datasets like COCO, our approach can still achieve moderate improvement, suggesting the proposed method is robust to crowdedness. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chu_Detection_in_Crowded_Scenes_One_Proposal_Multiple_Predictions_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09163 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chu_Detection_in_Crowded_Scenes_One_Proposal_Multiple_Predictions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chu_Detection_in_Crowded_Scenes_One_Proposal_Multiple_Predictions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chu_Detection_in_Crowded_CVPR_2020_supplemental.zip | null | null |
Background Data Resampling for Outlier-Aware Classification | Yi Li, Nuno Vasconcelos | The problem of learning an image classifier that allows detection of out-of-distribution (OOD) examples, with the help of auxiliary background datasets, is studied. While training with background has been shown to improve OOD detection performance, the optimal choice of such dataset remains an open question, and challenges of data imbalance and computational complexity make it a potentially inefficient or even impractical solution. Targeted at balancing between efficiency and detection quality, a dataset resampling approach is proposed for obtaining a compact yet representative set of background data points. The resampling algorithm takes inspiration from prior work on hard negative mining, performing an iterative adversarial weighting on the background examples and using the learned weights to obtain the subset of desired size. Experiments on different datasets, model architectures and training strategies validate the universal effectiveness and efficiency of adversarially resampled background data. Code is available at https://github.com/JerryYLi/ bg-resample-ood. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Background_Data_Resampling_for_Outlier-Aware_Classification_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Background_Data_Resampling_for_Outlier-Aware_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Background_Data_Resampling_for_Outlier-Aware_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Background_Data_Resampling_CVPR_2020_supplemental.pdf | null | null |
Prime Sample Attention in Object Detection | Yuhang Cao, Kai Chen, Chen Change Loy, Dahua Lin | It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily result in higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, on the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, e.g. OHEM and Focal Loss, consistently by around 2% on both single-stage and two-stage detectors, even with a strong backbone ResNeXt-101. Code is available at: https://github.com/open-mmlab/mmdetection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cao_Prime_Sample_Attention_in_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.04821 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Prime_Sample_Attention_in_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Prime_Sample_Attention_in_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cao_Prime_Sample_Attention_CVPR_2020_supplemental.pdf | null | null |
Learning Temporal Co-Attention Models for Unsupervised Video Action Localization | Guoqiang Gong, Xinghan Wang, Yadong Mu, Qi Tian | Temporal action localization (TAL) in untrimmed videos recently receives tremendous research enthusiasm. To our best knowledge, this is the first attempt in the literature to explore this task under an unsupervised setting, hereafter referred to as action co-localization (ACL), where only the total count of unique actions that appear in the video set is known. To solve ACL, we propose a two-step "clustering + localization" iterative procedure. The clustering step provides noisy pseudo-labels for the localization step, and the localization step provides temporal co-attention models that in turn improve the clustering performance. Using such two-step procedure, weakly-supervised TAL can be regarded as a direct extension of our ACL model. Technically, our contributions are two-folds: 1) temporal co-attention models, either class-specific or class-agnostic, learned from video-level labels or pseudo-labels in an iterative reinforced fashion; 2) new losses specially designed for ACL, including action-background separation loss and cluster-based triplet loss. Comprehensive evaluations are conducted on 20-action THUMOS14 and 100-action ActivityNet-1.2. On both benchmarks, the proposed model for ACL exhibits strong performances, even surprisingly comparable with state-of-the-art weakly-supervised methods. For example, previous best weakly-supervised model achieves 26.8% under [email protected] on THUMOS14, our new records are 30.1% (weakly-supervised) and 25.0% (unsupervised). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gong_Learning_Temporal_Co-Attention_Models_for_Unsupervised_Video_Action_Localization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gong_Learning_Temporal_Co-Attention_Models_for_Unsupervised_Video_Action_Localization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gong_Learning_Temporal_Co-Attention_Models_for_Unsupervised_Video_Action_Localization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
NAS-FCOS: Fast Neural Architecture Search for Object Detection | Ning Wang, Yang Gao, Hao Chen, Peng Wang, Zhi Tian, Chunhua Shen, Yanning Zhang | The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_NAS-FCOS_Fast_Neural_Architecture_Search_for_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_NAS-FCOS_Fast_Neural_Architecture_Search_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_NAS-FCOS_Fast_Neural_Architecture_Search_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Enhancing Generic Segmentation With Learned Region Representations | Or Isaacs, Oran Shayer, Michael Lindenbaum | Deep learning approaches to generic (non-semantic) segmentation have so far been indirect and relied on edge detection. This is in contrast to semantic segmentation, where DNNs are applied directly. We propose an alternative approach called Deep Generic Segmentation (DGS) and try to follow the path used for semantic segmentation. Our main contribution is a new method for learning a pixel-wise representation that reflects segment relatedness. This representation is combined with a CRF to yield the segmentation algorithm. We show that we are able to learn meaningful representations that improve segmentation quality and that the representations themselves achieve state-of-the-art segment similarity scores. The segmentation results are competitive and promising. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Isaacs_Enhancing_Generic_Segmentation_With_Learned_Region_Representations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.08564 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Isaacs_Enhancing_Generic_Segmentation_With_Learned_Region_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Isaacs_Enhancing_Generic_Segmentation_With_Learned_Region_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Isaacs_Enhancing_Generic_Segmentation_CVPR_2020_supplemental.pdf | null | null |
What's Hidden in a Randomly Weighted Neural Network? | Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari | Training a neural network is synonymous with learning the values of the weights. By contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive performance without ever training the weight values. Hidden in a randomly weighted Wide ResNet-50 is a subnetwork (with random weights) that is smaller than, but matches the performance of a ResNet-34 trained on ImageNet. Not only do these "untrained subnetworks" exist, but we provide an algorithm to effectively find them. We empirically show that as randomly weighted neural networks with fixed weights grow wider and deeper, an "untrained subnetwork" approaches a network with learned weights in accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ramanujan_Whats_Hidden_in_a_Randomly_Weighted_Neural_Network_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramanujan_Whats_Hidden_in_a_Randomly_Weighted_Neural_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramanujan_Whats_Hidden_in_a_Randomly_Weighted_Neural_Network_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ramanujan_Whats_Hidden_in_CVPR_2020_supplemental.pdf | null | null |
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation | Myeongjin Kim, Hyeran Byun | Since annotating pixel-level labels for semantic segmentation is laborious, leveraging synthetic data is an attractive solution. However, due to the domain gap between synthetic domain and real domain, it is challenging for a model trained with synthetic data to generalize to real data. In this paper, considering the fundamental difference between the two domains as the texture, we propose a method to adapt to the target domain's texture. First, we diversity the texture of synthetic images using a style transfer algorithm. The various textures of generated images prevent a segmentation model from overfitting to one specific (synthetic) texture. Then, we fine-tune the model with self-training to get direct supervision of the target texture. Our results achieve state-of-the-art performance and we analyze the properties of the model trained on the stylized dataset with extensive experiments. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Learning_Texture_Invariant_Representation_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00867 | https://www.youtube.com/watch?v=T1iQQzURKS0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Learning_Texture_Invariant_Representation_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Learning_Texture_Invariant_Representation_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
VQA With No Questions-Answers Training | Ben-Zion Vatashsky, Shimon Ullman | Methods for teaching machines to answer visual questions have made significant progress in recent years, but current methods still lack important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answers and handling new domains without explicit examples. We propose a novel method that consists of two main parts: generating a question graph representation, and an answering procedure, guided by the abstract structure of the question graph to invoke an extendable set of visual estimators. Training is performed for the language part and the visual part on their own, but unlike existing schemes, the method does not require any training using images with associated questions and answers. This approach is able to handle novel domains (extended question types and new object classes, properties and relations) as long as corresponding visual estimators are available. In addition, it can provide explanations to its answers and suggest alternatives when questions are not grounded in the image. We demonstrate that this approach achieves both high performance and domain extensibility without any questions-answers training. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Vatashsky_VQA_With_No_Questions-Answers_Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/1811.08481 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Vatashsky_VQA_With_No_Questions-Answers_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Vatashsky_VQA_With_No_Questions-Answers_Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Vatashsky_VQA_With_No_CVPR_2020_supplemental.pdf | null | null |
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model | Han Fu, Rui Wu, Chenghao Liu, Jianling Sun | Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community. One of the most popular research topics in this domain is Food Retrieval, due to its profound influence on health-oriented applications. In this paper, we focus on the task of cross-modal retrieval between food images and cooking recipes. We present Modality-Consistent Embedding Network (MCEN) that learns modality-invariant representations by projecting images and texts to the same embedding space. To capture the latent alignments between modalities, we incorporate stochastic latent variables to explicitly exploit the interactions between textual and visual features. Importantly, our method learns the cross-modal alignments during training but computes embeddings of different modalities independently at inference time for the sake of efficiency. Extensive experimental results clearly demonstrate that the proposed MCEN outperforms all existing approaches on the benchmark Recipe1M dataset and requires less computational cost. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fu_MCEN_Bridging_Cross-Modal_Gap_between_Cooking_Recipes_and_Dish_Images_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01095 | https://www.youtube.com/watch?v=aCn7X-XcQsM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fu_MCEN_Bridging_Cross-Modal_Gap_between_Cooking_Recipes_and_Dish_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fu_MCEN_Bridging_Cross-Modal_Gap_between_Cooking_Recipes_and_Dish_Images_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fu_MCEN_Bridging_Cross-Modal_CVPR_2020_supplemental.pdf | null | null |
NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection | Yazhao Li, Yanwei Pang, Jianbing Shen, Jiale Cao, Ling Shao | Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-shot detectors make scale-aware predictions based on multiple pyramid layers. However, the features in the pyramid are not scale-aware enough, which limits the detection performance. Two common problems in single-shot detectors caused by object scale variations can be observed: (1) small objects are easily missed; (2) the salient part of a large object is sometimes detected as an object. With this observation, we propose a new Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid features and explore scale-aware features. In NET, a Neighbor Erasing Module (NEM) is designed to erase the salient features of large objects and emphasize the features of small objects in shallow layers. A Neighbor Transferring Module (NTM) is introduced to transfer the erased features and highlight large objects in deep layers. With this mechanism, a single-shot network called NETNet is constructed for scale-aware object detection. In addition, we propose to aggregate nearest neighboring pyramid features to enhance our NET. NETNet achieves 38.5% AP at a speed of 27 FPS and 32.0% AP at a speed of 55 FPS on MS COCO dataset. As a result, NETNet achieves a better trade-off for real-time and accurate object detection. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_NETNet_Neighbor_Erasing_and_Transferring_Network_for_Better_Single_Shot_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.06690 | https://www.youtube.com/watch?v=WrNG6ZAyzR0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_NETNet_Neighbor_Erasing_and_Transferring_Network_for_Better_Single_Shot_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_NETNet_Neighbor_Erasing_and_Transferring_Network_for_Better_Single_Shot_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Detailed 2D-3D Joint Representation for Human-Object Interaction | Yong-Lu Li, Xinpeng Liu, Han Lu, Shiyi Wang, Junqi Liu, Jiefeng Li, Cewu Lu | Human-Object Interaction (HOI) detection lies at the core of action understanding. Besides 2D information such as human/object appearance and locations, 3D pose is also usually utilized in HOI learning since its view-independence. However, rough 3D body joints just carry sparse body information and are not sufficient to understand complex interactions. Thus, we need detailed 3D body shape to go further. Meanwhile, the interacted object in 3D is also not fully studied in HOI learning. In light of these, we propose a detailed 2D-3D joint representation learning method. First, we utilize the single-view human body capture method to obtain detailed 3D body, face and hand shapes. Next, we estimate the 3D object location and size with reference to the 2D human-object spatial configuration and object category priors. Finally, a joint learning framework and cross-modal consistency tasks are proposed to learn the joint HOI representation. To better evaluate the 2D ambiguity processing capacity of models, we propose a new benchmark named Ambiguous-HOI consisting of hard ambiguous images. Extensive experiments in large-scale HOI benchmark and Ambiguous-HOI show impressive effectiveness of our method. Code and data are available at https://github.com/DirtyHarryLYL/DJ-RN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Detailed_2D-3D_Joint_Representation_for_Human-Object_Interaction_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08154 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Detailed_2D-3D_Joint_Representation_for_Human-Object_Interaction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Detailed_2D-3D_Joint_Representation_for_Human-Object_Interaction_CVPR_2020_paper.html | CVPR 2020 | null | https://cove.thecvf.com/datasets/323 | null |
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors | Edward Kim, Divya Gopinath, Corina Pasareanu, Sanjit A. Seshia | Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a programmatic and semantic approach to explaining, understanding, and debugging the correct and incorrect behaviors of a neural network based perception system. Our approach is semantic in that it employs a high-level representation of the distribution of environment scenarios that the detector is intended to work on. It is programmatic in that the representation is a program in a domain-specific probabilistic programming language using which synthetic data can be generated to train and test the neural network. We present a framework that assesses the performance of the neural network to identify correct and incorrect detections, extracts rules from those results that semantically characterizes the correct and incorrect scenarios, and then specializes the probabilistic program with those rules in order to more precisely characterize the scenarios in which the neural network operates correctly or not, without human intervention. We demonstrate our results using the Scenic probabilistic programming language and a neural network-based object detector. Our experiments show that it is possible to automatically generate compact rules that significantly increase the correct detection rate (or conversely the incorrect detection rate) of the network and can thus help with debugging and understanding its behavior. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_A_Programmatic_and_Semantic_Approach_to_Explaining_and_Debugging_Neural_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=3qZLVPzEL1s | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_A_Programmatic_and_Semantic_Approach_to_Explaining_and_Debugging_Neural_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_A_Programmatic_and_Semantic_Approach_to_Explaining_and_Debugging_Neural_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_A_Programmatic_and_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/316 | null |
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks | Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, Qinghua Hu | Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity. To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore, we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction. The proposed ECA module is both efficient and effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFlops vs. 3.86 GFlops, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_ECA-Net_Efficient_Channel_Attention_for_Deep_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ipZ2AS1b0rI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_ECA-Net_Efficient_Channel_Attention_for_Deep_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_ECA-Net_Efficient_Channel_Attention_for_Deep_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_ECA-Net_Efficient_Channel_CVPR_2020_supplemental.pdf | null | null |
Geometry and Learning Co-Supported Normal Estimation for Unstructured Point Cloud | Haoran Zhou, Honghua Chen, Yidan Feng, Qiong Wang, Jing Qin, Haoran Xie, Fu Lee Wang, Mingqiang Wei, Jun Wang | In this paper, we propose a normal estimation method for unstructured point cloud. We observe that geometric estimators commonly focus more on feature preservation but are hard to tune parameters and sensitive to noise, while learning-based approaches pursue an overall normal estimation accuracy but cannot well handle challenging regions such as surface edges. This paper presents a novel normal estimation method, under the co-support of geometric estimator and deep learning. To lowering the learning difficulty, we first propose to compute a suboptimal initial normal at each point by searching for a best fitting patch. Based on the computed normal field, we design a normal-based height map network (NH-Net) to fine-tune the suboptimal normals. Qualitative and quantitative evaluations demonstrate the clear improvements of our results over both traditional methods and learning-based methods, in terms of estimation accuracy and feature recovery. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Geometry_and_Learning_Co-Supported_Normal_Estimation_for_Unstructured_Point_Cloud_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=YEOyOt-uMxU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Geometry_and_Learning_Co-Supported_Normal_Estimation_for_Unstructured_Point_Cloud_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Geometry_and_Learning_Co-Supported_Normal_Estimation_for_Unstructured_Point_Cloud_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_Geometry_and_Learning_CVPR_2020_supplemental.pdf | null | null |
DR Loss: Improving Object Detection by Distributional Ranking | Qi Qian, Lei Chen, Hao Li, Rong Jin | Most of object detection algorithms can be categorized into two classes: two-stage detectors and one-stage detectors. Recently, many efforts have been devoted to one-stage detectors for the simple yet effective architecture. Different from two-stage detectors, one-stage detectors aim to identify foreground objects from all candidates in a single stage. This architecture is efficient but can suffer from the imbalance issue with respect to two aspects: the inter-class imbalance between the number of candidates from foreground and background classes and the intra-class imbalance in the hardness of background candidates, where only a few candidates are hard to be identified. In this work, we propose a novel distributional ranking (DR) loss to handle the challenge. For each image, we convert the classification problem to a ranking problem, which considers pairs of candidates within the image, to address the inter-class imbalance problem. Then, we push the distributions of confidence scores for foreground and background towards the decision boundary. After that, we optimize the rank of the expectations of derived distributions in lieu of original pairs. Our method not only mitigates the intra-class imbalance issue in background candidates but also improves the efficiency for the ranking algorithm. By merely replacing the focal loss in RetinaNet with the developed DR loss and applying ResNet-101 as the backbone, mAP of the single-scale test on COCO can be improved from 39.1% to 41.7% without bells and whistles, which demonstrates the effectiveness of the proposed loss function. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qian_DR_Loss_Improving_Object_Detection_by_Distributional_Ranking_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.10156 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qian_DR_Loss_Improving_Object_Detection_by_Distributional_Ranking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qian_DR_Loss_Improving_Object_Detection_by_Distributional_Ranking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qian_DR_Loss_Improving_CVPR_2020_supplemental.pdf | null | null |
End-to-End Camera Calibration for Broadcast Videos | Long Sha, Jennifer Hobbs, Panna Felsen, Xinyu Wei, Patrick Lucey, Sujoy Ganguly | The increasing number of vision-based tracking systems deployed in production have necessitated fast, robust camera calibration. In the domain of sport, the majority of current work focuses on sports where lines and intersections are easy to extract, and appearance is relatively consistent across venues. However, for more challenging sports like basketball, those techniques are not sufficient. In this paper, we propose an end-to-end approach for single moving camera calibration across challenging scenarios in sports. Our method contains three key modules: 1) area-based court segmentation, 2) camera pose estimation with embedded templates, 3) homography prediction via a spatial transform network (STN). All three modules are connected, enabling end-to-end training. We evaluate our method on a new college basketball dataset and demonstrate state of the art performance in variable and dynamic environments. We also validate our method on the World Cup 2014 dataset to show its competitive performance against the state-of-the-art methods. Lastly, we show that our method is two orders of magnitude faster than the previous state of the art on both datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sha_End-to-End_Camera_Calibration_for_Broadcast_Videos_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sha_End-to-End_Camera_Calibration_for_Broadcast_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sha_End-to-End_Camera_Calibration_for_Broadcast_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Selective Transfer With Reinforced Transfer Network for Partial Domain Adaptation | Zhihong Chen, Chao Chen, Zhaowei Cheng, Boyuan Jiang, Ke Fang, Xinyu Jin | One crucial aspect of partial domain adaptation (PDA) is how to select the relevant source samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on their high-level information (deep features). However, since the domain shift between source and target domains, only using the deep features for sample selection is defective. We argue that it is more reasonable to additionally exploit the pixel-level information for PDA problem, as the appearance difference between outlier source classes and target classes is significantly large. In this paper, we propose a reinforced transfer network (RTNet), which utilizes both high-level and pixel-level information for PDA problem. Our RTNet is composed of a reinforced data selector (RDS) based on reinforcement learning (RL), which filters out the outlier source samples, and a domain adaptation model which minimizes the domain discrepancy in the shared label space. Specifically, in the RDS, we design a novel reward based on the reconstruct errors of selected source samples on the target generator, which introduces the pixel-level information to guide the learning of RDS. Besides, we develope a state containing high-level information, which used by the RDS for sample selection. The proposed RDS is a general module, which can be easily integrated into existing DA models to make them fit the PDA situation. Extensive experiments indicate that RTNet can achieve state-of-the-art performance for PDA tasks on several benchmark datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Selective_Transfer_With_Reinforced_Transfer_Network_for_Partial_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.10756 | https://www.youtube.com/watch?v=uyKUD0QB_po | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Selective_Transfer_With_Reinforced_Transfer_Network_for_Partial_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Selective_Transfer_With_Reinforced_Transfer_Network_for_Partial_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Neural Head Reenactment with Latent Pose Descriptors | Egor Burkov, Igor Pasechnik, Artur Grigorev, Victor Lempitsky | We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Burkov_Neural_Head_Reenactment_with_Latent_Pose_Descriptors_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.12000 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Burkov_Neural_Head_Reenactment_with_Latent_Pose_Descriptors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Burkov_Neural_Head_Reenactment_with_Latent_Pose_Descriptors_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SaccadeNet: A Fast and Accurate Object Detector | Shiyi Lan, Zhou Ren, Yi Wu, Larry S. Davis, Gang Hua | Object detection is an essential step towards holistic scene understanding. Most existing object detection algorithms attend to certain object areas once and then predict the object locations. However, scientists have revealed that human do not look at the scene in fixed steadiness. Instead, human eyes move around, locating informative parts to understand the object location. This active perceiving movement process is called saccade. In this paper, inspired by such mechanism, we propose a fast and accurate object detector called SaccadeNet. It contains four main modules, the Center Attentive Module, the Corner Attentive Module, the Attention Transitive Module, and the Aggregation Attentive Module, which allows it to attend to different informative object keypoints actively, and predict object locations from coarse to fine. The Corner Attentive Module is used only during training to extract more informative corner features which brings free-lunch performance boost. On the MS COCO dataset, we achieve the performance of 40.4% mAP at 28 FPS and 30.5% mAP at 118 FPS. Among all the real-time object detectors, our SaccadeNet achieves the best detection performance, which demonstrates the effectiveness of the proposed detection mechanism. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lan_SaccadeNet_A_Fast_and_Accurate_Object_Detector_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12125 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lan_SaccadeNet_A_Fast_and_Accurate_Object_Detector_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lan_SaccadeNet_A_Fast_and_Accurate_Object_Detector_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lan_SaccadeNet_A_Fast_CVPR_2020_supplemental.pdf | null | null |
Learning Augmentation Network via Influence Functions | Donghoon Lee, Hyunsin Park, Trung Pham, Chang D. Yoo | Data augmentation can impact the generalization performance of an image classification model in a significant way. However, it is currently conducted on the basis of trial and error, and its impact on the generalization performance cannot be predicted during training. This paper considers an influence function that predicts how generalization performance, in terms of validation loss, is affected by a particular augmented training sample. The influence function provides an approximation of the change in validation loss without actually comparing the performances that include and exclude the sample in the training process. Based on this function, a differentiable augmentation network is learned to augment an input training sample to reduce validation loss. The augmented sample is fed into the classification network, and its influence is approximated as a function of the parameters of the last fully-connected layer of the classification network. By backpropagating the influence to the augmentation network, the augmentation network parameters are learned. Experimental results on CIFAR-10, CIFAR-100, and ImageNet show that the proposed method provides better generalization performance than conventional data augmentation methods do. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Learning_Augmentation_Network_via_Influence_Functions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Learning_Augmentation_Network_via_Influence_Functions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Learning_Augmentation_Network_via_Influence_Functions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_Learning_Augmentation_Network_CVPR_2020_supplemental.pdf | null | null |
Self-Robust 3D Point Recognition via Gather-Vector Guidance | Xiaoyi Dong, Dongdong Chen, Hang Zhou, Gang Hua, Weiming Zhang, Nenghai Yu | In this paper, we look into the problem of 3D adversary attack, and propose to leverage the internal properties of the point clouds and the adversarial examples to design a new self-robust deep neural network (DNN) based 3D recognition systems. As a matter of fact, on one hand, point clouds are highly structured. Hence for each local part of clean point clouds, it is possible to learn what is it ("part of a bottle") and its relative position ("upper part of a bottle") to the global object center. On the other hand, with the visual quality constraint, 3D adversarial samples often only produce small local perturbations, thus they will roughly keep the original global center but may cause incorrect local relative position estimation. Motivated by these two properties, we use relative position (dubbed as "gather-vector") as the adversarial indicator and propose a new robust gather module. Equipped with this module, we further propose a new self-robust 3D point recognition network. Through extensive experiments, we demonstrate that the proposed method can improve the robustness of the target attack under the white-box setting significantly. For I-FGSM based attack, our method reduces the attack success rate from 94.37 % to 75.69 %. For C&W based attack, our method reduces the attack success rate more than 40.00 %. Moreover, our method is complementary to other types of defense methods to achieve better defense results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_Self-Robust_3D_Point_Recognition_via_Gather-Vector_Guidance_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Self-Robust_3D_Point_Recognition_via_Gather-Vector_Guidance_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Self-Robust_3D_Point_Recognition_via_Gather-Vector_Guidance_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dong_Self-Robust_3D_Point_CVPR_2020_supplemental.pdf | null | null |
RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge | Jun Cheng, Fuxiang Wu, Yanling Tian, Lei Wang, Dapeng Tao | Text-to-image synthesis is a challenging task that generates realistic images from a textual sequence, which usually contains limited information compared with the corresponding image and so is ambiguous and abstractive. The limited textual information only describes a scene partly, which will complicate the generation with complementing the other details implicitly and lead to low-quality images. To address this problem, we propose a novel rich feature generating text-to-image synthesis, called RiFeGAN, to enrich the given description. In order to provide additional visual details and avoid conflicting, RiFeGAN exploits an attention-based caption matching model to select and refine the compatible candidate captions from prior knowledge. Given enriched captions, RiFeGAN uses self-attentional embedding mixtures to extract features across them effectually and handle the diverging features further. Then it exploits multi-captions attentional generative adversarial networks to synthesize images from those features. The experiments conducted on widely-used datasets show that the models can generate images from enriched captions effectually and improve the results significantly. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cheng_RiFeGAN_Rich_Feature_Generation_for_Text-to-Image_Synthesis_From_Prior_Knowledge_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=PcFzjrodm50 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_RiFeGAN_Rich_Feature_Generation_for_Text-to-Image_Synthesis_From_Prior_Knowledge_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_RiFeGAN_Rich_Feature_Generation_for_Text-to-Image_Synthesis_From_Prior_Knowledge_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cheng_RiFeGAN_Rich_Feature_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem | Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars | This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high capacity server interacts with a myriad of resource-limited edge devices, imposing strong requirements on scalability and local data privacy. We aim to address this challenge within the continual learning paradigm and provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints. First, on the server, we introduce incremental learning of task-specific expert models, subsequently aggregated using a concealed unsupervised user prior. Aggregation avoids retraining, whereas the user prior conceals sensitive raw user data, and grants unsupervised adaptation. Second, local user-adaptation incorporates a domain adaptation point of view, adapting regularizing batch normalization parameters to the user data. We explore various empirical user configurations with different priors in categories and a tenfold of transforms for MIT Indoor Scene recognition, and classify numbers in a combined MNIST and SVHN setup. Extensive experiments yield promising results for data-driven local adaptation and elicit user priors for server adaptation to depend on the model rather than user data. Hence, although user-adaptation remains a challenging open problem, the DUA framework formalizes a principled foundation for personalizing both on server and user device, while maintaining privacy and scalability. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13296 | https://www.youtube.com/watch?v=YdCwErjIcVg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning From Noisy Anchors for One-Stage Object Detection | Hengduo Li, Zuxuan Wu, Chen Zhu, Caiming Xiong, Richard Socher, Larry S. Davis | State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their intersection-over-union (IoU) with corresponding ground-truth objects. Such a harsh split conditioned on IoU results in binary labels that are potentially noisy and challenging for training. In this paper, we propose to mitigate noise incurred by imperfect label assignment such that the contributions of anchors are dynamically determined by a carefully constructed cleanliness score associated with each anchor. Exploring outputs from both regression and classification branches, the cleanliness scores, estimated without incurring any additional computational overhead, are used not only as soft labels to supervise the training of the classification branch but also sample re-weighting factors for improved localization and classification accuracy. We conduct extensive experiments on COCO, and demonstrate, among other things, the proposed approach steadily improves RetinaNet by 2% with various backbones. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Learning_From_Noisy_Anchors_for_One-Stage_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.05086 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_From_Noisy_Anchors_for_One-Stage_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_From_Noisy_Anchors_for_One-Stage_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Interactions and Relationships Between Movie Characters | Anna Kukleva, Makarand Tapaswi, Ivan Laptev | Interactions between people are often governed by their relationships. On the flip side, social relationships are built upon several interactions. Two strangers are more likely to greet and introduce themselves while becoming friends over time. We are fascinated by this interplay between interactions and relationships, and believe that it is an important aspect of understanding social situations. In this work, we propose neural models to learn and jointly predict interactions, relationships, and the pair of characters that are involved. We note that interactions are informed by a mixture of visual and dialog cues, and present a multimodal architecture to extract meaningful information from them. Localizing the pair of interacting characters in video is a time-consuming process, instead, we train our model to learn from clip-level weak labels. We evaluate our models on the MovieGraphs dataset and show the impact of modalities, use of longer temporal context for predicting relationships, and achieve encouraging performance using weak labels as compared with ground-truth labels. Code is online. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kukleva_Learning_Interactions_and_Relationships_Between_Movie_Characters_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13158 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kukleva_Learning_Interactions_and_Relationships_Between_Movie_Characters_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kukleva_Learning_Interactions_and_Relationships_Between_Movie_Characters_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kukleva_Learning_Interactions_and_CVPR_2020_supplemental.pdf | null | null |
MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment | Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi | Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable success achieved, there is a broad consensus that training DCNNs heavily relies on massive annotated data. Unfortunately, IQA is a typical small sample problem. Therefore, most of the existing DCNN-based IQA metrics operate based on pre-trained networks. However, these pre-trained networks are not designed for IQA task, leading to generalization problem when evaluating different types of distortions. With this motivation, this paper presents a no-reference IQA metric based on deep meta-learning. The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily. Specifically, we first collect a number of NR-IQA tasks for different distortions. Then meta-learning is adopted to learn the prior knowledge shared by diversified distortions. Finally, the quality prior model is fine-tuned on a target NR-IQA task for quickly obtaining the quality model. Extensive experiments demonstrate that the proposed metric outperforms the state-of-the-arts by a large margin. Furthermore, the meta-model learned from synthetic distortions can also be easily generalized to authentic distortions, which is highly desired in real-world applications of IQA metrics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_MetaIQA_Deep_Meta-Learning_for_No-Reference_Image_Quality_Assessment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05508 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_MetaIQA_Deep_Meta-Learning_for_No-Reference_Image_Quality_Assessment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_MetaIQA_Deep_Meta-Learning_for_No-Reference_Image_Quality_Assessment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
RetinaTrack: Online Single Stage Joint Detection and Tracking | Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang | Traditionally multi-object tracking and object detection are performed using separate systems with most prior works focusing exclusively on one of these aspects over the other. Tracking systems clearly benefit from having access to accurate detections, however and there is ample evidence in literature that detectors can benefit from tracking which, for example, can help to smooth predictions over time. In this paper we focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical. We propose a conceptually simple and efficient joint model of detection and tracking, called RetinaTrack, which modifies the popular single stage RetinaNet approach such that it is amenable to instance-level embedding training. We show, via evaluations on the Waymo Open Dataset, that we outperform a recent state of the art tracking algorithm while requiring significantly less computation. We believe that our simple yet effective approach can serve as a strong baseline for future work in this area. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lu_RetinaTrack_Online_Single_Stage_Joint_Detection_and_Tracking_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13870 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_RetinaTrack_Online_Single_Stage_Joint_Detection_and_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_RetinaTrack_Online_Single_Stage_Joint_Detection_and_Tracking_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
End-to-End 3D Point Cloud Instance Segmentation Without Detection | Haiyong Jiang, Feilong Yan, Jianfei Cai, Jianmin Zheng, Jun Xiao | 3D instance segmentation plays a predominant role in environment perception of robotics and augmented reality. Many deep learning based methods have been presented recently for this task. These methods rely on either a detection branch to propose objects or a grouping step to assemble same-instance points. However, detection based methods do not ensure a consistent instance label for each point, while the grouping step requires parameter-tuning and is computationally expensive. In this paper, we introduce a novel framework to enable end-to-end instance segmentation without detection and a separate step of grouping. The core idea is to convert instance segmentation to a candidate assignment problem. At first, a set of instance candidates is sampled. Then we propose an assignment module for candidate assignment and a suppression module to eliminate redundant candidates. A mapping between instance labels and instance candidates is further sought to construct an instance grouping loss for the network training. Experimental results demonstrate that our method is more effective and efficient than previous approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_End-to-End_3D_Point_Cloud_Instance_Segmentation_Without_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_End-to-End_3D_Point_Cloud_Instance_Segmentation_Without_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_End-to-End_3D_Point_Cloud_Instance_Segmentation_Without_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Noise-Aware Fully Webly Supervised Object Detection | Yunhang Shen, Rongrong Ji, Zhiwei Chen, Xiaopeng Hong, Feng Zheng, Jianzhuang Liu, Mingliang Xu, Qi Tian | We investigate the emerging task of learning object detectors with sole image-level labels on the web without requiring any other supervision like precise annotations or additional images from well-annotated benchmark datasets. Such a task, termed as fully webly supervised object detection, is extremely challenging, since image-level labels on the web are always noisy, leading to poor performance of the learned detectors. In this work, we propose an end-to-end framework to jointly learn webly supervised detectors and reduce the negative impact of noisy labels. Such noise is heterogeneous, which is further categorized into two types, namely background noise and foreground noise. Regarding the background noise, we propose a residual learning structure incorporated with weakly supervised detection, which decomposes background noise and models clean data. To explicitly learn the residual feature between clean data and noisy labels, we further propose a spatially-sensitive entropy criterion, which exploits the conditional distribution of detection results to estimate the confidence of background categories being noise. Regarding the foreground noise, a bagging-mixup learning is introduced, which suppresses foreground noisy signals from incorrectly labelled images, whilst maintaining the diversity of training data. We evaluate the proposed approach on popular benchmark datasets by training detectors on web images, which are retrieved by the corresponding category tags from photo-sharing sites. Extensive experiments show that our method achieves significant improvements over the state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shen_Noise-Aware_Fully_Webly_Supervised_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Noise-Aware_Fully_Webly_Supervised_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Noise-Aware_Fully_Webly_Supervised_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation | Jianzhun Shao, Yuhang Jiang, Gu Wang, Zhigang Li, Xiangyang Ji | 6D pose estimation from a single RGB image is a challenging and vital task in computer vision. The current mainstream deep model methods resort to 2D images annotated with real-world ground-truth 6D object poses, whose collection is fairly cumbersome and expensive, even unavailable in many cases. In this work, to get rid of the burden of 6D annotations, we formulate the 6D pose refinement as a Markov Decision Process and impose on the reinforcement learning approach with only 2D image annotations as weakly-supervised 6D pose information, via a delicate reward definition and a composite reinforced optimization method for efficient and effective policy training. Experiments on LINEMOD and T-LESS datasets demonstrate that our Pose-Free approach is able to achieve state-of-the-art performance compared with the methods without using real-world ground-truth 6D pose labels. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shao_PFRL_Pose-Free_Reinforcement_Learning_for_6D_Pose_Estimation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_PFRL_Pose-Free_Reinforcement_Learning_for_6D_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shao_PFRL_Pose-Free_Reinforcement_Learning_for_6D_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shao_PFRL_Pose-Free_Reinforcement_CVPR_2020_supplemental.pdf | null | null |
Robust Learning Through Cross-Task Consistency | Amir R. Zamir, Alexander Sax, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, Leonidas J. Guibas | Visual perception entails solving a wide set of tasks (e.g., object detection, depth estimation, etc). The predictions made for different tasks out of one image are not independent, and therefore, are expected to be 'consistent'. We propose a flexible and fully computational framework for learning while enforcing Cross-Task Consistency (X-TAC). The proposed formulation is based on 'inference path invariance' over an arbitrary graph of prediction domains. We observe that learning with cross-task consistency leads to more accurate predictions, better generalization to out-of-distribution samples, and improved sample efficiency. This framework also leads to a powerful unsupervised quantity, called 'Consistency Energy, based on measuring the intrinsic consistency of the system. Consistency Energy well correlates with the supervised error (r=0.67), thus it can be employed as an unsupervised robustness metric as well as for detection of out-of-distribution inputs (AUC=0.99). The evaluations were performed on multiple datasets, including Taskonomy, Replica, CocoDoom, and ApolloScape. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.04096 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Exploring Self-Attention for Image Recognition | Hengshuang Zhao, Jiaya Jia, Vladlen Koltun | Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness for image recognition. We consider two forms of self-attention. One is pairwise self-attention, which generalizes standard dot-product attention and is fundamentally a set operator. The other is patchwise self-attention, which is strictly more powerful than convolution. Our pairwise self-attention networks match or outperform their convolutional counterparts, and the patchwise models substantially outperform the convolutional baselines. We also conduct experiments that probe the robustness of learned representations and conclude that self-attention networks may have significant benefits in terms of robustness and generalization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Exploring_Self-Attention_for_Image_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.13621 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Exploring_Self-Attention_for_Image_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Exploring_Self-Attention_for_Image_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Shape correspondence using anisotropic Chebyshev spectral CNNs | Qinsong Li, Shengjun Liu, Ling Hu, Xinru Liu | Establishing correspondence between shapes is a very important and active research topic in many domains. Due to the powerful ability of deep learning on geometric data, lots of attractive results have been achieved by convolutional neural networks (CNNs). In this paper, we propose a novel architecture for shape correspondence, termed Anisotropic Chebyshev spectral CNNs (ACSCNNs), based on a new extension of the manifold convolution operator. The extended convolution operators aggregate the local features of signals by a set of oriented kernels around each point, which allows to much more comprehensively capture the intrinsic signal information. Rather than using fixed oriented kernels in the spatial domain in previous CNNs, in our framework, the kernels are learned by spectral filtering, based on the eigen-decompositions of multiple Anisotropic Laplace-Beltrami Operators. To reduce the computational complexity, we employ an explicit expansion of the Chebyshev polynomial basis to represent the spectral filters whose expansion coefficients are trainable. Through the benchmark experiments of shape correspondence, our architecture is demonstrated to be efficient and be able to provide better than the state-of-the-art results in several datasets even if using constant functions as inputs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Shape_correspondence_using_anisotropic_Chebyshev_spectral_CNNs_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Shape_correspondence_using_anisotropic_Chebyshev_spectral_CNNs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Shape_correspondence_using_anisotropic_Chebyshev_spectral_CNNs_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Uncertainty-Aware Score Distribution Learning for Action Quality Assessment | Yansong Tang, Zanlin Ni, Jiahuan Zhou, Danyang Zhang, Jiwen Lu, Ying Wu, Jie Zhou | Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where finer-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distribution learning (MUSDL) method to explore the disentangled components of a score. In order to demonstrate the effectiveness of our proposed methods, We conduct experiments on two AQA datasets containing various Olympic actions. Our approaches set new state-of-the-arts under the Spearman's Rank Correlation (i.e., 0.8102 on AQA-7 and 0.9273 on MTL-AQA). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_Uncertainty-Aware_Score_Distribution_Learning_for_Action_Quality_Assessment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.07665 | https://www.youtube.com/watch?v=ejykgyDF4hA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Uncertainty-Aware_Score_Distribution_Learning_for_Action_Quality_Assessment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Uncertainty-Aware_Score_Distribution_Learning_for_Action_Quality_Assessment_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tang_Uncertainty-Aware_Score_Distribution_CVPR_2020_supplemental.pdf | null | null |
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather | Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, Felix Heide | The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare "edge-case" scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000 km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here https://github.com/princeton-computational-imaging/SeeingThroughFog. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bijelic_Seeing_Through_Fog_Without_Seeing_Fog_Deep_Multimodal_Sensor_Fusion_CVPR_2020_paper.pdf | http://arxiv.org/abs/1902.08913 | https://www.youtube.com/watch?v=HPT4nsCkT5Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bijelic_Seeing_Through_Fog_Without_Seeing_Fog_Deep_Multimodal_Sensor_Fusion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bijelic_Seeing_Through_Fog_Without_Seeing_Fog_Deep_Multimodal_Sensor_Fusion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bijelic_Seeing_Through_Fog_CVPR_2020_supplemental.pdf | null | null |
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition | Jinhyung Kim, Seunghwan Cha, Dongyoon Wee, Soonmin Bae, Junmo Kim | Deep neural networks for video action recognition frequently require 3D convolutional filters and often encounter overfitting due to a larger number of parameters. In this paper, we propose Random Mean Scaling (RMS), a simple and effective regularization method, to relieve the overfitting problem in 3D residual networks. The key idea of RMS is to randomly vary the magnitude of low-frequency components of the feature to regularize the model. The low-frequency component can be derived by a spatio-temporal mean on the local patch of a feature. We present that selective regularization on this locally smoothed feature makes a model handle the low-frequency and high-frequency component distinctively, resulting in performance improvement. RMS can enhance a model with little additional computation only during training, similar to other regularization methods. RMS also can be incorporated into typical training process without any bells and whistles. Experimental results show the improvement in generalization performance on a popular action recognition datasets demonstrating the effectiveness of RMS as a regularization technique, compared to other state-of-the-art regularization methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Regularization_on_Spatio-Temporally_Smoothed_Feature_for_Action_Recognition_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=PUMp7Mbxjs0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Regularization_on_Spatio-Temporally_Smoothed_Feature_for_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Regularization_on_Spatio-Temporally_Smoothed_Feature_for_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_Regularization_on_Spatio-Temporally_CVPR_2020_supplemental.pdf | null | null |
Learning Invariant Representation for Unsupervised Image Restoration | Wenchao Du, Hu Chen, Hongyu Yang | Recently, cross domain transfer has been applied for unsupervised image restoration tasks. However, directly applying existing frameworks would lead to domain-shift problems in translated images due to lack of effective supervision. Instead, we propose an unsupervised learning method that explicitly learns invariant presentation from noisy data and reconstructs clear observations. To do so, we introduce discrete disentangling representation and adversarial domain adaption into general domain transfer framework, aided by extra self-supervised modules including background and semantic consistency constraints, learning robust representation under dual domain constraints, such as feature and image domains. Experiments on synthetic and real noise removal tasks show the proposed method achieves comparable performance with other stateof-the-art supervised and unsupervised methods, while having faster and stable convergence than other domain adaption methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Du_Learning_Invariant_Representation_for_Unsupervised_Image_Restoration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12769 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_Learning_Invariant_Representation_for_Unsupervised_Image_Restoration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_Learning_Invariant_Representation_for_Unsupervised_Image_Restoration_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Nanoscale Motion Patterns of Vesicles in Living Cells | Arif Ahmed Sekh, Ida Sundvor Opstad, Asa Birna Birgisdottir, Truls Myrmel, Balpreet Singh Ahluwalia, Krishna Agarwal, Dilip K. Prasad | Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution ( 250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow residual attention network to make it possible for the first time to analysis sub-resolution motion patterns in vesicles that may also be of sub-resolution diameter. Our results show state-of-the-art performance, 89% validation accuracy on simulated dataset and 82% testing accuracy on an experimental dataset of living heart muscle cells imaged under three different pathological conditions. We demonstrate automated analysis of the motion states and changed in them for over 9000 vesicles. Such analysis will enable large scale biological studies of vesicle transport and interaction in living cells in the future. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sekh_Learning_Nanoscale_Motion_Patterns_of_Vesicles_in_Living_Cells_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sekh_Learning_Nanoscale_Motion_Patterns_of_Vesicles_in_Living_Cells_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sekh_Learning_Nanoscale_Motion_Patterns_of_Vesicles_in_Living_Cells_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sekh_Learning_Nanoscale_Motion_CVPR_2020_supplemental.pdf | null | null |
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio | Zhengsu Chen, Jianwei Niu, Lingxi Xie, Xuefeng Liu, Longhui Wei, Qi Tian | Automatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the network architecture search methods. This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs, so that under each network configuration, one can estimate the FLOPs utilization ratio (FUR) for each layer and use it to determine whether to increase or decrease the number of channels on the layer. Note that FUR, like the gradient of a non-linear function, is accurate only in a small neighborhood of the current network. Hence, we design an iterative mechanism so that the initial network undergoes a number of steps, each of which has a small 'adjusting rate' to control the changes to the network. The computational overhead of the entire search process is reasonable, i.e., comparable to that of re-training the final model from scratch. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach, which consistently outperforms the pruning counterpart. The code is available at https://github.com/danczs/NetworkAdjustment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Network_Adjustment_Channel_Search_Guided_by_FLOPs_Utilization_Ratio_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02767 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Network_Adjustment_Channel_Search_Guided_by_FLOPs_Utilization_Ratio_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Network_Adjustment_Channel_Search_Guided_by_FLOPs_Utilization_Ratio_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching | Rui Liu, Chengxi Yang, Wenxiu Sun, Xiaogang Wang, Hongsheng Li | Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by CycleGAN show great potential in dealing with domain gap, it is non-trivial to generalize this method to stereo matching due to the problem of pixel distortion and stereo mismatch after translation. In this paper, we propose an end-to-end training framework with domain translation and stereo matching networks to tackle this challenge. First, joint optimization between domain translation and stereo matching networks in our end-to-end framework makes the former facilitate the latter one to the maximum extent. Second, this framework introduces two novel losses, i.e., bidirectional multi-scale feature re-projection loss and correlation consistency loss, to help translate all synthetic stereo images into realistic ones as well as maintain epipolar constraints. The effective combination of above two contributions leads to impressive stereo-consistent translation and disparity estimation accuracy. In addition, a mode seeking regularization term is added to endow the synthetic-to-real translation results with higher fine-grained diversity. Extensive experiments demonstrate the effectiveness of the proposed framework on bridging the synthetic-to-real domain gap on stereo matching. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01927 | https://www.youtube.com/watch?v=Ume9kIkI_ZQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation | Shaokai Ye, Kailu Wu, Mu Zhou, Yunfei Yang, Sia Huat Tan, Kaidi Xu, Jiebo Song, Chenglong Bao, Kaisheng Ma | Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target domain and do not properly handle the trade-off between the source domain and the target domain. In this work, instead of training a classifier to adapt to the target domain, we use a separable component called data calibrator to help the fixed source classifier recover discrimination power in the target domain, while preserving the source domain's performance. When the difference between two domains is small, the source classifier's representation is sufficient to perform well in the target domain and outperforms GAN-based methods in digits. Otherwise, the proposed method can leverage synthetic images generated by GANs to boost performance and achieve state-of-the-art performance in digits datasets and driving scene semantic segmentation. Our method also empirically suggests the potential connection between domain adaptation and adversarial attacks. Code release is available at https://github.com/yeshaokai/ Calibrator-Domain-Adaptation | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ye_Light-weight_Calibrator_A_Separable_Component_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.12796 | https://www.youtube.com/watch?v=eTsUR_yAgCk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Light-weight_Calibrator_A_Separable_Component_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Light-weight_Calibrator_A_Separable_Component_for_Unsupervised_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ye_Light-weight_Calibrator_A_CVPR_2020_supplemental.pdf | null | null |
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation | Dengsheng Chen, Jun Li, Zheng Wang, Kai Xu | We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent space of a deep generative model of canonical 3D shapes with normalized pose. We train a variational auto-encoder (VAE) for generating 3D point clouds in the canonical space from an RGBD image. The VAE is trained in a cross-category fashion, exploiting the publicly available large 3D shape repositories. Since the 3D point cloud is generated in normalized pose (with actual size), the encoder of the VAE learns view-factorized RGBD embedding. It maps an RGBD image in arbitrary view into a poseindependent 3D shape representation. Object pose is then estimated via contrasting it with a pose-dependent feature of the input RGBD extracted with a separate deep neural networks. We integrate the learning of CASS and pose and size estimation into an end-to-end trainable network, achieving the state-of-the-art performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.09322 | https://www.youtube.com/watch?v=_XO3ybwAdsM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Spatial RNN Codec for End-to-End Image Compression | Chaoyi Lin, Jiabao Yao, Fangdong Chen, Li Wang | Recently, deep learning has been explored as a promising direction for image compression. Removing the spatial redundancy of the image is crucial for image compression and most learning based methods focus on removing the redundancy between adjacent pixels. Intuitively, to explore larger pixel range beyond adjacent pixel is beneficial for removing the redundancy. In this paper, we propose a fast yet effective method for end-to-end image compression by incorporating a novel spatial recurrent neural network. Block based LSTM is utilized to remove the redundant information between adjacent pixels and blocks. Besides, the proposed method is a potential efficient system that parallel computation on individual blocks is possible. Experimental results demonstrate that the proposed model outperforms state-of-the-art traditional image compression standards and learning based image compression models in terms of both PSNR and MS-SSIM metrics. It provides a 26.73% bits-reduction than High Efficiency Video Coding (HEVC), which is the current official state-of-the-art video codec. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_A_Spatial_RNN_Codec_for_End-to-End_Image_Compression_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_A_Spatial_RNN_Codec_for_End-to-End_Image_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_A_Spatial_RNN_Codec_for_End-to-End_Image_Compression_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Two Causal Principles for Improving Visual Dialog | Jiaxin Qi, Yulei Niu, Jianqiang Huang, Hanwang Zhang | This paper unravels the design tricks adopted by us, the champion team MReaL-BDAI, for Visual Dialog Challenge 2019: two causal principles for improving Visual Dialog (VisDial). By "improving", we mean that they can promote almost every existing VisDial model to the state-of-the-art performance on the leader-board. Such a major improvement is only due to our careful inspection on the causality behind the model and data, finding that the community has overlooked two causalities in VisDial. Intuitively, Principle 1 suggests: we should remove the direct input of the dialog history to the answer model, otherwise a harmful shortcut bias will be introduced; Principle 2 says: there is an unobserved confounder for history, question, and answer, leading to spurious correlations from training data. In particular, to remove the confounder suggested in Principle 2, we propose several causal intervention algorithms, which make the training fundamentally different from the traditional likelihood estimation. Note that the two principles are model-agnostic, so they are applicable in any VisDial model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qi_Two_Causal_Principles_for_Improving_Visual_Dialog_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10496 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_Two_Causal_Principles_for_Improving_Visual_Dialog_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_Two_Causal_Principles_for_Improving_Visual_Dialog_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qi_Two_Causal_Principles_CVPR_2020_supplemental.pdf | null | null |
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