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LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models | https://openreview.net/forum?id=qCUWVT0Ayy | https://openreview.net/forum?id=qCUWVT0Ayy | Zecheng Tang,Chenfei Wu,Juntao Li,Nan Duan | ICLR 2024,Poster | Graphic layout generation, a growing research field, plays a significant role in user engagement and information perception.
Existing methods primarily treat layout generation as a numerical optimization task, focusing on quantitative aspects while overlooking the semantic information of layout, such as the relationship between each layout element.
In this paper, we propose LayoutNUWA, the first model that treats layout generation as a code generation task to enhance semantic information and harness the hidden layout expertise of large language models~(LLMs).
Concretely, we develop a Code Instruct Tuning (CIT) approach comprising three interconnected modules: 1) the Code Initialization (CI) module quantifies the numerical conditions and initializes them as HTML code with strategically placed masks; 2) the Code Completion (CC) module employs the formatting knowledge of LLMs to fill in the masked portions within the HTML code; 3) the Code Rendering (CR) module transforms the completed code into the final layout output, ensuring a highly interpretable and transparent layout generation procedure that directly maps code to a visualized layout. We attain significant state-of-the-art performance (even over 50\% improvements compared to previous works) on multiple datasets, showcasing the strong capabilities of LayoutNUWA. | https://openreview.net/pdf/2bf32a4e8fc17b59e6c0880cd07d30b9b219eded.pdf |
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models | https://openreview.net/forum?id=oZDJKTlOUe | https://openreview.net/forum?id=oZDJKTlOUe | Yiyang Zhou,Chenhang Cui,Jaehong Yoon,Linjun Zhang,Zhun Deng,Chelsea Finn,Mohit Bansal,Huaxiu Yao | ICLR 2024,Poster | Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. However, LVLMs still suffer from object hallucination, which is the problem of generating descriptions that include objects that do not actually exist in the images. This can negatively impact many vision-language tasks, such as visual summarization and reasoning. To address this issue, we propose a simple yet powerful algorithm, LVLM Hallucination Revisor (LURE), to post-hoc rectify object hallucination in LVLMs by reconstructing less hallucinatory descriptions. LURE is grounded in a rigorous statistical analysis of the key factors underlying object hallucination, including co-occurrence (the frequent appearance of certain objects alongside others in images), uncertainty (objects with higher uncertainty during LVLM decoding), and object position (hallucination often appears in the later part of the generated text). LURE can also be seamlessly integrated with any LVLMs. We evaluate LURE on six open-source LVLMs and found it outperforms the previous best approach in both general object hallucination evaluation metrics, GPT, and human evaluations. | https://openreview.net/pdf/f81e3a4330cbf440b5e213cd43956c14da3d935e.pdf |
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks | https://openreview.net/forum?id=MeB86edZ1P | https://openreview.net/forum?id=MeB86edZ1P | Mingqing Xiao,Qingyan Meng,Zongpeng Zhang,Di He,Zhouchen Lin | ICLR 2024,Poster | Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from catastrophic forgetting. How could neuronal operations solve this problem is an important question for AI and neuroscience. Many previous studies draw inspiration from observed neuroscience phenomena and propose episodic replay or synaptic metaplasticity, but they are not guaranteed to explicitly preserve knowledge for neuron populations. Other works focus on machine learning methods with more mathematical grounding, e.g., orthogonal projection on high dimensional spaces, but there is no neural correspondence for neuromorphic computing. In this work, we develop a new method with neuronal operations based on lateral connections and Hebbian learning, which can protect knowledge by projecting activity traces of neurons into an orthogonal subspace so that synaptic weight update will not interfere with old tasks. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities and enable orthogonal projection. This provides new insights into how neural circuits and Hebbian learning can help continual learning, and also how the concept of orthogonal projection can be realized in neuronal systems. Our method is also flexible to utilize arbitrary training methods based on presynaptic activities/traces. Experiments show that our method consistently solves forgetting for spiking neural networks with nearly zero forgetting under various supervised training methods with different error propagation approaches, and outperforms previous approaches under various settings. Our method can pave a solid path for building continual neuromorphic computing systems. The code is available at https://github.com/pkuxmq/HLOP-SNN. | https://openreview.net/pdf/4a2267430d5c9d1e9d526acf2246f897ba51f90b.pdf |
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models | https://openreview.net/forum?id=Th6NyL07na | https://openreview.net/forum?id=Th6NyL07na | Yung-Sung Chuang,Yujia Xie,Hongyin Luo,Yoon Kim,James R. Glass,Pengcheng He | ICLR 2024,Poster | Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that does not require conditioning on retrieved external knowledge nor additional fine-tuning. Our approach obtains the next-token distribution by contrasting the differences in logits obtained from projecting the later layers versus earlier layers to the vocabulary space, exploiting the fact that factual knowledge in an LLMs has generally been shown to be localized to particular transformer layers. We find that this **D**ecoding by C**o**ntrasting **La**yers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts. DoLa consistently improves the truthfulness across multiple choices tasks and open-ended generation tasks, for example improving the performance of LLaMA family models on TruthfulQA by 12-17% absolute points, demonstrating its potential in making LLMs reliably generate truthful facts. | https://openreview.net/pdf/19e5f84dd62746917a78017994b93a8a4608cb3f.pdf |
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion | https://openreview.net/forum?id=ZNzDXDFZ0B | https://openreview.net/forum?id=ZNzDXDFZ0B | Tianhao Wu,Chuanxia Zheng,Tat-Jen Cham | ICLR 2024,Poster | Generating complete 360\textdegree{} panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available. Existing GAN-based approaches face some barriers to achieving higher quality output, and have poor generalization performance over different mask types. In this paper, we present our 360\textdegree{} indoor RGB panorama outpainting model using latent diffusion models (LDM), called PanoDiffusion. We introduce a new bi-modal latent diffusion structure that utilizes both RGB and depth panoramic data during training, which works surprisingly well to outpaint depth-free RGB images during inference. We further propose a novel technique of introducing progressive camera rotations during each diffusion denoising step, which leads to substantial improvement in achieving panorama wraparound consistency. Results show that our PanoDiffusion not only significantly outperforms state-of-the-art methods on RGB panorama outpainting by producing diverse well-structured results for different types of masks, but can also synthesize high-quality depth panoramas to provide realistic 3D indoor models. | https://openreview.net/pdf/a7fde35aead101007e47375b180b626040938ac8.pdf |
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation | https://openreview.net/forum?id=sJ88Wg5Bp5 | https://openreview.net/forum?id=sJ88Wg5Bp5 | Jiaming Liu,Senqiao Yang,Peidong Jia,Renrui Zhang,Ming Lu,Yandong Guo,Wei Xue,Shanghang Zhang | ICLR 2024,Poster | Since real-world machine systems are running in non-stationary environments, Continual Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually changing target domains. Recently, existing methods mainly focus on model-based adaptation, which aims to leverage a self-training manner to extract the target domain knowledge. However, pseudo labels can be noisy and the updated model parameters are unreliable under dynamic data distributions, leading to error accumulation and catastrophic forgetting in the continual adaptation process. To tackle these challenges and maintain the model plasticity, we design a Visual Domain Adapter (ViDA) for CTTA, explicitly handling both domain-specific and domain-shared knowledge. Specifically, we first comprehensively explore the different domain representations of the adapters with trainable high-rank or low-rank embedding spaces. Then we inject ViDAs into the pre-trained model, which leverages high-rank and low-rank features to adapt the current domain distribution and maintain the continual domain-shared knowledge, respectively. To exploit the low-rank and high-rank ViDAs more effectively, we further propose a Homeostatic Knowledge Allotment (HKA) strategy, which adaptively combines different knowledge from each ViDA. Extensive experiments conducted on four widely used benchmarks demonstrate that our proposed method achieves state-of-the-art performance in both classification and segmentation CTTA tasks. Note that, our method can be regarded as a novel transfer paradigm for large-scale models, delivering promising results in adaptation to continually changing distributions. | https://openreview.net/pdf/b7fa358de7ec06e71ed430a72e3685fa9c856543.pdf |
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models | https://openreview.net/forum?id=Spp2i1hKwV | https://openreview.net/forum?id=Spp2i1hKwV | Shaokun Zhang,Xiaobo Xia,Zhaoqing Wang,Ling-Hao Chen,Jiale Liu,Qingyun Wu,Tongliang Liu | ICLR 2024,Poster | In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving strong performance. However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs. To address this challenge, this paper introduces an influence-driven selective annotation method that aims to minimize annotation costs while improving the quality of in-context examples. The essence of our method is to select a pivotal subset from a large-scale unlabeled data pool to annotate for the subsequent sampling of prompts. Specifically, a directed graph is first constructed to represent unlabeled data. Afterward, the influence of candidate unlabeled subsets is quantified with a diffusion process. A simple yet effective greedy algorithm for unlabeled data selection is lastly introduced. It iteratively selects the data if it provides a maximum marginal gain with respect to quantified influence. Compared with previous efforts on selective annotations, our influence-driven method works in an end-to-end manner, avoids an intractable explicit balance between data diversity and representativeness, and enjoys theoretical support. Experiments confirm the superiority of the proposed method on various benchmarks, achieving better performance under lower time consumption during subset selection. | https://openreview.net/pdf/64bee015027876634043808c8b68bbdd59d33d93.pdf |
Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data | https://openreview.net/forum?id=ttXg3SKAg5 | https://openreview.net/forum?id=ttXg3SKAg5 | Yuhui Zhang,Elaine Sui,Serena Yeung | ICLR 2024,Poster | Building cross-modal applications is challenging due to limited paired multi-modal data. Recent works have shown that leveraging a pre-trained multi-modal contrastive representation space enables cross-modal tasks to be learned from uni-modal data. This is based on the assumption that contrastive optimization makes embeddings from different modalities interchangeable. However, this assumption is under-explored due to the poorly understood geometry of the multi-modal contrastive space, where a modality gap exists. In our study, we provide a theoretical explanation of this space's geometry and introduce a three-step method, $C^3$ (Connect, Collapse, Corrupt), to bridge the modality gap, enhancing the interchangeability of embeddings. Our $C^3$ method significantly improves cross-modal learning from uni-modal data, achieving state-of-the-art results on zero-shot image / audio / video captioning and text-to-image generation. | https://openreview.net/pdf/c1f311c477377c3fc6367ea7a67d81f07a0bec56.pdf |
Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence | https://openreview.net/forum?id=fQHb1uZzl7 | https://openreview.net/forum?id=fQHb1uZzl7 | Sunghwan Hong,Seokju Cho,Seungryong Kim,Stephen Lin | ICLR 2024,Poster | This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. In the context of dense matching, many works benefit from one of two forms of aggregation: feature aggregation, which pertains to the alignment of similar features, or cost aggregation, a procedure aimed at instilling coherence in the flow estimates across neighboring pixels. In this work, we first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes. We then introduce a simple yet effective architecture that harnesses self- and cross-attention mechanisms to show that our approach unifies feature aggregation and cost aggregation and effectively harnesses the strengths of both techniques. Within the proposed attention layers, the features and cost volume both complement each other, and the attention layers are interleaved through a coarse-to-fine design to further promote accurate correspondence estimation. Finally at inference, our network produces multi-scale predictions, computes their confidence scores, and selects the most confident flow for final prediction. Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods. | https://openreview.net/pdf/93cb77d1d2a1d462d387cc8e2b6ca77461126a7e.pdf |
Data-independent Module-aware Pruning for Hierarchical Vision Transformers | https://openreview.net/forum?id=7Ol6foUi1G | https://openreview.net/forum?id=7Ol6foUi1G | Yang He,Joey Tianyi Zhou | ICLR 2024,Poster | Hierarchical vision transformers (ViTs) have two advantages over conventional ViTs. First, hierarchical ViTs achieve linear computational complexity with respect to image size by local self-attention. Second, hierarchical ViTs create hierarchical feature maps by merging image patches in deeper layers for dense prediction. However, existing pruning methods ignore the unique properties of hierarchical ViTs and use the magnitude value as the weight importance. This approach leads to two main drawbacks. First, the "local" attention weights are compared at a "global" level, which may cause some "locally" important weights to be pruned due to their relatively small magnitude "globally". The second issue with magnitude pruning is that it fails to consider the distinct weight distributions of the network, which are essential for extracting coarse to fine-grained features at various hierarchical levels.
To solve the aforementioned issues, we have developed a Data-independent Module-Aware Pruning method (DIMAP) to compress hierarchical ViTs. To ensure that "local" attention weights at different hierarchical levels are compared fairly in terms of their contribution, we treat them as a **module** and examine their contribution by analyzing their information distortion. Furthermore, we introduce a novel weight metric that is solely based on weights and does not require input images, thereby eliminating the **dependence** on the patch merging process. Our method validates its usefulness and strengths on Swin Transformers of different sizes on ImageNet-1k classification. Notably, the top-5 accuracy drop is only 0.07% when we remove 52.5% FLOPs and 52.7% parameters of Swin-B. When we reduce 33.2% FLOPs and 33.2% parameters of Swin-S, we can even achieve a 0.8% higher relative top-5 accuracy than the original model. Code is available at: [https://github.com/he-y/Data-independent-Module-Aware-Pruning](https://github.com/he-y/Data-independent-Module-Aware-Pruning). | https://openreview.net/pdf/71c5b33a80576458937a84e97e365d99ec6d7daf.pdf |
Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement | https://openreview.net/forum?id=RDSTjtnqCg | https://openreview.net/forum?id=RDSTjtnqCg | Kai Xu,Rongyu Chen,Gianni Franchi,Angela Yao | ICLR 2024,Poster | Activation shaping has proven highly effective for identifying out-of-distribution (OOD) samples post-hoc. Activation shaping prunes and scales network activations before estimating the OOD energy score; such an extremely simple approach achieves state-of-the-art OOD detection with minimal in-distribution (ID) accuracy drops. This paper analyzes the working mechanism behind activation shaping. We directly show that the benefits for OOD detection derive only from scaling, while pruning is detrimental. Based on our analysis, we propose SCALE, an even simpler yet more effective post-hoc network enhancement method for OOD detection. SCALE attains state-of-the-art OOD detection performance without any compromises on ID accuracy. Furthermore, we integrate scaling concepts into learning and propose Intermediate Tensor SHaping (ISH) for training-time OOD detection enhancement. ISH achieves significant AUROC improvements for both near- and far-OOD, highlighting the importance of activation distributions in emphasizing ID data characteristics. Our code and models are available at https://github.com/kai422/SCALE. | https://openreview.net/pdf/22e1d7e0d22c9a7aa0a9d68919417bcafaa2ecd0.pdf |
A Simple and Effective Pruning Approach for Large Language Models | https://openreview.net/forum?id=PxoFut3dWW | https://openreview.net/forum?id=PxoFut3dWW | Mingjie Sun,Zhuang Liu,Anna Bair,J Zico Kolter | ICLR 2024,Poster | As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance. Existing methods, however, require either retraining, which is rarely affordable for billion-scale LLMs, or solving a weight reconstruction problem reliant on second-order information, which may also be computationally expensive. In this paper, we introduce a novel, straightforward yet effective pruning method, termed Wanda (Pruning by Weights and activations), designed to induce sparsity in pretrained LLMs. Motivated by the recent observation of emergent large magnitude features in LLMs, our approach prunes weights with the smallest magnitudes multiplied by the corresponding input activations, on a per-output basis. Notably, Wanda requires no retraining or weight update, and the pruned LLM can be used as is. We conduct a thorough evaluation of our method Wanda on LLaMA and LLaMA-2 across various language benchmarks. Wanda significantly outperforms the established baseline of magnitude pruning and performs competitively against recent method involving intensive weight update. | https://openreview.net/pdf/11822fd63761d2e1fdfdf4c98ffd4db1b62b4ae4.pdf |
GeoLLM: Extracting Geospatial Knowledge from Large Language Models | https://openreview.net/forum?id=TqL2xBwXP3 | https://openreview.net/forum?id=TqL2xBwXP3 | Rohin Manvi,Samar Khanna,Gengchen Mai,Marshall Burke,David B. Lobell,Stefano Ermon | ICLR 2024,Poster | The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power.
Here we explore the question of whether the vast amounts of knowledge found in Internet language corpora, now compressed within large language models (LLMs), can be leveraged for geospatial prediction tasks.
We first demonstrate that LLMs embed remarkable spatial information about locations, but
naively querying LLMs using geographic coordinates alone is ineffective in predicting key indicators like population density.
We then present GeoLLM, a novel method that can effectively extract geospatial knowledge from LLMs with auxiliary map data from OpenStreetMap.
We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods.
Across these tasks, our method demonstrates a 70\% improvement in performance (measured using Pearson's $r^2$) relative to baselines that use nearest neighbors or use information directly from the prompt, and performance equal to or exceeding satellite-based benchmarks in the literature.
With GeoLLM, we observe that GPT-3.5 outperforms Llama 2 and RoBERTa by 19\% and 51\% respectively, suggesting that the performance of our method scales well with the size of the model and its pretraining dataset.
Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
Crucially, GeoLLM shows promise in mitigating the limitations of existing geospatial covariates and complementing them well.
Code is available on the project website: https://rohinmanvi.github.io/GeoLLM | https://openreview.net/pdf/02994a8ffea21a68e43ae2abfeeefeb238d01096.pdf |
Instant3D: Fast Text-to-3D with Sparse-view Generation and Large Reconstruction Model | https://openreview.net/forum?id=2lDQLiH1W4 | https://openreview.net/forum?id=2lDQLiH1W4 | Jiahao Li,Hao Tan,Kai Zhang,Zexiang Xu,Fujun Luan,Yinghao Xu,Yicong Hong,Kalyan Sunkavalli,Greg Shakhnarovich,Sai Bi | ICLR 2024,Poster | Text-to-3D with diffusion models has achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from slow inference, low diversity and Janus problems, or are feed-forward methods that generate low-quality results due to the scarcity of 3D training data. In this paper, we propose Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner. We adopt a two-stage paradigm, which first generates a sparse set of four structured and consistent views from text in one shot with a fine-tuned 2D text-to-image diffusion model, and then directly regresses the NeRF from the generated images with a novel transformer-based sparse-view reconstructor. Through extensive experiments, we demonstrate that our method can generate diverse 3D assets of high visual quality within 20 seconds, which is two orders of magnitude faster than previous optimization-based methods that can take 1 to 10 hours. Our project webpage is: https://jiahao.ai/instant3d/. | https://openreview.net/pdf/5260d71892f69d77662ae32856d6ed13f372c279.pdf |
Effective and Efficient Federated Tree Learning on Hybrid Data | https://openreview.net/forum?id=py4ZV2qYQI | https://openreview.net/forum?id=py4ZV2qYQI | Qinbin Li,Chulin Xie,Xiaojun Xu,Xiaoyuan Liu,Ce Zhang,Bo Li,Bingsheng He,Dawn Song | ICLR 2024,Poster | Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, most existing federated learning studies focus on either horizontal or vertical data settings, where the data of different parties are assumed to be from the same feature or sample space. In practice, a common scenario is the hybrid data setting, where data from different parties may differ both in the features and samples. To address this, we propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data. We observe the existence of consistent split rules in trees. With the help of these split rules, we theoretically show that the knowledge of parties can be incorporated into the lower layers of a tree. Based on our theoretical analysis, we propose a layer-level solution that does not need frequent communication traffic to train a tree. Our experiments demonstrate that HybridTree can achieve comparable accuracy to the centralized setting with low computational and communication overhead. HybridTree can achieve up to 8 times speedup compared with the other baselines. | https://openreview.net/pdf/ff388e62b5cbb5e1d45cd3ef17f5a656cab65b8b.pdf |
Knowledge Distillation Based on Transformed Teacher Matching | https://openreview.net/forum?id=MJ3K7uDGGl | https://openreview.net/forum?id=MJ3K7uDGGl | Kaixiang Zheng,EN-HUI YANG | ICLR 2024,Poster | As a technique to bridge logit matching and probability distribution matching, temperature scaling plays a pivotal role in knowledge distillation (KD). Conventionally, temperature scaling is applied to both teacher's logits and student's logits in KD. Motivated by some recent works, in this paper, we drop instead temperature scaling on the student side, and systematically study the resulting variant of KD, dubbed transformed teacher matching (TTM). By reinterpreting temperature scaling as a power transform of probability distribution, we show that in comparison with the original KD, TTM has an inherent Rényi entropy term in its objective function, which serves as an extra regularization term. Extensive experiment results demonstrate that thanks to this inherent regularization, TTM leads to trained students with better generalization than the original KD. To further enhance student's capability to match teacher's power transformed probability distribution, we introduce a sample-adaptive weighting coefficient into TTM, yielding a novel distillation approach dubbed weighted TTM (WTTM). It is shown, by comprehensive experiments, that although WTTM is simple, it is effective, improves upon TTM, and achieves state-of-the-art accuracy performance. Our source code is available at https://github.com/zkxufo/TTM. | https://openreview.net/pdf/0daf5adec83ebd3d6f7991ab1036797a15b10db8.pdf |
Image Translation as Diffusion Visual Programmers | https://openreview.net/forum?id=yozwqhIHXj | https://openreview.net/forum?id=yozwqhIHXj | Cheng Han,James Chenhao Liang,Qifan Wang,MAJID RABBANI,Sohail Dianat,Raghuveer Rao,Ying Nian Wu,Dongfang Liu | ICLR 2024,Poster | We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion model within the GPT architecture, orchestrating a coherent sequence of visual programs ($i.e.$, computer vision models) for various pro-symbolic steps, which span RoI identification, style transfer, and position manipulation, facilitating transparent and controllable image translation processes. Extensive experiments demonstrate DVP’s remarkable performance, surpassing concurrent arts. This success can be attributed to several key features of DVP: First, DVP achieves condition-flexible translation via instance normalization, enabling the model to eliminate sensitivity caused by the manual guidance and optimally focus on textual descriptions for high-quality content generation. Second, the frame work enhances in-context reasoning by deciphering intricate high-dimensional concepts in feature spaces into more accessible low-dimensional symbols ($e.g.$, [Prompt], [RoI object]), allowing for localized, context-free editing while maintaining overall coherence. Last but not least, DVP improves systemic controllability and explainability by offering explicit symbolic representations at each programming stage, empowering users to intuitively interpret and modify results. Our research marks a substantial step towards harmonizing artificial image translation processes with cognitive intelligence, promising broader applications. | https://openreview.net/pdf/dc4fa789446c6f2c8a588bc9e92aa4e49783f261.pdf |
Raidar: geneRative AI Detection viA Rewriting | https://openreview.net/forum?id=bQWE2UqXmf | https://openreview.net/forum?id=bQWE2UqXmf | Chengzhi Mao,Carl Vondrick,Hao Wang,Junfeng Yang | ICLR 2024,Poster | We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves. | https://openreview.net/pdf/107e60d1800d244fc256b316a5a1b2cc4f56fac3.pdf |
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning | https://openreview.net/forum?id=KjegfPGRde | https://openreview.net/forum?id=KjegfPGRde | Zhengxiang Shi,Aldo Lipani | ICLR 2024,Poster | Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT). PT stands out from other PEFT approaches because it maintains competitive performance with fewer trainable parameters and does not drastically scale up its parameters as the model size expands. However, PT introduces additional soft prompt tokens, leading to longer input sequences, which significantly impacts training and inference time and memory usage due to the Transformer's quadratic complexity. Particularly concerning for Large Language Models (LLMs) that face heavy daily querying. To address this issue, we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt into a shorter soft prompt and a pair of low-rank matrices that are then optimised with two different learning rates. This allows DePT to achieve better performance while saving substantial memory and time costs compared to vanilla PT and its variants, without changing trainable parameter sizes. Through extensive experiments on 23 natural language processing (NLP) and vision-language (VL) tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches, including the full fine-tuning baseline, in some scenarios. Additionally, we empirically show that DEPT grows more efficient as the model size increases. Our further study reveals that DePT integrates seamlessly with parameter-efficient transfer learning in the few-shot learning setting and highlights its adaptability to various model architectures and sizes. | https://openreview.net/pdf/989eff8252e97852e238423f4d470061cd60b8fd.pdf |
Multi-View Representation is What You Need for Point-Cloud Pre-Training | https://openreview.net/forum?id=imZcqOrbig | https://openreview.net/forum?id=imZcqOrbig | Siming Yan,Chen Song,Youkang Kong,Qixing Huang | ICLR 2024,Poster | A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks. Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction. We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss, which enforces the 2D projections of the 3D feature to be consistent with the output of pre-trained 2D networks. To prevent the feature from discarding 3D signals, we introduce the multi-view consistency loss that additionally encourages the projected 2D feature representations to capture pixel-wise correspondences across different views. Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D shape classification, part segmentation, 3D object detection, and semantic segmentation, achieving state-of-the-art performance. | https://openreview.net/pdf/9e89aefbb4f98da78a08d05d128e95f089016a04.pdf |
VDT: General-purpose Video Diffusion Transformers via Mask Modeling | https://openreview.net/forum?id=Un0rgm9f04 | https://openreview.net/forum?id=Un0rgm9f04 | Haoyu Lu,Guoxing Yang,Nanyi Fei,Yuqi Huo,Zhiwu Lu,Ping Luo,Mingyu Ding | ICLR 2024,Poster | This work introduces Video Diffusion Transformer (VDT), which pioneers the use of transformers in diffusion-based video generation.
It features transformer blocks with modularized temporal and spatial attention modules to leverage the rich spatial-temporal representation inherited in transformers. Additionally, we propose a unified spatial-temporal mask modeling mechanism, seamlessly integrated with the model, to cater to diverse video generation scenarios.
VDT offers several appealing benefits. (1) It excels at capturing temporal dependencies to produce temporally consistent video frames and even simulate the physics and dynamics of 3D objects over time. (2) It facilitates flexible conditioning information, e.g., simple concatenation in the token space, effectively unifying different token lengths and modalities. (3) Pairing with our proposed spatial-temporal mask modeling mechanism, it becomes a general-purpose video diffuser for harnessing a range of tasks, including unconditional generation, video prediction, interpolation, animation, and completion, etc. Extensive experiments on these tasks spanning various scenarios, including autonomous driving, natural weather, human action, and physics-based simulation, demonstrate the effectiveness of VDT. Moreover, we provide a comprehensive study on the capabilities of VDT in capturing accurate temporal dependencies, handling conditioning information, and the spatial-temporal mask modeling mechanism. Additionally, we present comprehensive studies on how VDT handles conditioning information with the mask modeling mechanism, which we believe will benefit future research and advance the field. Codes and models are available at the https://VDT-2023.github.io. | https://openreview.net/pdf/8781429d598437687744d54f5e6102be5c4ed7cd.pdf |
InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules | https://openreview.net/forum?id=aHmNpLlUlb | https://openreview.net/forum?id=aHmNpLlUlb | Yanqi Bao,Tianyu Ding,Jing Huo,Wenbin Li,Yuxin Li,Yang Gao | ICLR 2024,Poster | Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce **InsertNeRF**, a method for **INS**tilling g**E**ne**R**alizabili**T**y into **NeRF**. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings.
Code will be available at: https://github.com/bbbbby-99/InsertNeRF. | https://openreview.net/pdf/d57dfe9403dd15e18a17ae29d14e988f4dc29e9b.pdf |
Augmenting Transformers with Recursively Composed Multi-grained Representations | https://openreview.net/forum?id=u859gX7ADC | https://openreview.net/forum?id=u859gX7ADC | Xiang Hu,Qingyang Zhu,Kewei Tu,Wei Wu | ICLR 2024,Poster | We present ReCAT, a recursive composition augmented Transformer that is able to explicitly model hierarchical syntactic structures of raw texts without relying on gold trees during both learning and inference.
Existing research along this line restricts data to follow a hierarchical tree structure and thus lacks inter-span communications.
To overcome the problem, we propose a novel contextual inside-outside (CIO) layer that learns contextualized representations of spans through bottom-up and top-down passes, where a bottom-up pass forms representations of high-level spans by composing low-level spans, while a top-down pass combines information inside and outside a span. By stacking several CIO layers between the embedding layer and the attention layers in Transformer, the ReCAT model can perform both deep intra-span and deep inter-span interactions, and thus generate multi-grained representations fully contextualized with other spans.
Moreover, the CIO layers can be jointly pre-trained with Transformers, making ReCAT enjoy scaling ability, strong performance, and interpretability at the same time. We conduct experiments on various sentence-level and span-level tasks. Evaluation results indicate that ReCAT can significantly outperform vanilla Transformer models on all span-level tasks and recursive models on natural language inference tasks. More interestingly, the hierarchical structures induced by ReCAT exhibit strong consistency with human-annotated syntactic trees, indicating good interpretability brought by the CIO layers. | https://openreview.net/pdf/7367af5d7bd6ea44c44d64949f6af74374956854.pdf |
P2Seg: Pointly-supervised Segmentation via Mutual Distillation | https://openreview.net/forum?id=B4vzu2aokv | https://openreview.net/forum?id=B4vzu2aokv | Zipeng Wang,Xuehui Yu,Xumeng Han,Wenwen Yu,Zhixun Huang,Jianbin Jiao,Zhenjun Han | ICLR 2024,Poster | Point-level Supervised Instance Segmentation (PSIS) aims to enhance the applicability and scalability of instance segmentation by utilizing low-cost yet instance-informative annotations. Existing PSIS methods usually rely on positional information to distinguish objects, but predicting precise boundaries remains challenging due to the lack of contour annotations. Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions. In this paper, we design a Mutual Distillation Module (MDM) to leverage the complementary strengths of both instance position and semantic information and achieve accurate instance-level object perception. The MDM consists of Semantic to Instance (S2I) and Istance to Semantic (I2S). S2I is guided by the precise boundaries of semantic regions to learn the association between annotated points and instance contours. I2S leverages discriminative relationships between instances to facilitate the differentiation of various objects within the semantic map. Extensive experiments substantiate the efficacy of MDM in fostering the synergy between instance and semantic information, consequently improving the quality of instance-level object representations. Our method achieves 55.7 mAP50 and 17.6 mAP on the PASCAL VOC and MS COCO datasets, significantly outperforming recent PSIS methods and several box-supervised instance segmentation competitors. | https://openreview.net/pdf/23c457aa340e5e44d633da6ff0cb4ec67e360225.pdf |
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting | https://openreview.net/forum?id=7oLshfEIC2 | https://openreview.net/forum?id=7oLshfEIC2 | Shiyu Wang,Haixu Wu,Xiaoming Shi,Tengge Hu,Huakun Luo,Lintao Ma,James Y. Zhang,JUN ZHOU | ICLR 2024,Poster | Time series forecasting is widely used in extensive applications, such as traffic planning and weather forecasting. However, real-world time series usually present intricate temporal variations, making forecasting extremely challenging. Going beyond the mainstream paradigms of plain decomposition and multiperiodicity analysis, we analyze temporal variations in a novel view of multiscale-mixing, where time series present distinct patterns in different sampling scales. Specifically, the microscopic and the macroscopic information are reflected in fine and coarse scales, respectively, and thereby complex variations are inherently disentangled. Based on this observation, we propose TimeMixer as a fully MLP-based architecture with Past-Decomposable-Mixing (PDM) and Future-Multipredictor-Mixing (FMM) blocks to take full advantage of disentangled multiscale series in both past extraction and future prediction phases. Concretely, PDM applies the decomposition to multiscale series and further mixes the decomposed seasonal and trend components in fine-to-coarse and coarse-to-fine directions separately, which successively aggregates the microscopic seasonal and macroscopic trend information. FMM further ensembles multiple predictors to utilize complementary forecasting capabilities in multiscale observations. Consequently, our proposed TimeMixer is able to achieve consistent state-of-the-art performances in both long-term and short-term forecasting tasks with favorable run-time efficiency. | https://openreview.net/pdf/0b58c032dfedc0fb38afcc36295bec251de05ab4.pdf |
Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach | https://openreview.net/forum?id=7hxoYxKDTV | https://openreview.net/forum?id=7hxoYxKDTV | Shaofeng Zhang,Jinfa Huang,Qiang Zhou,zhibin wang,Fan Wang,Jiebo Luo,Junchi Yan | ICLR 2024,Poster | Image outpainting aims to generate the content of an input sub-image beyond its original boundaries. It is an important task in content generation yet remains an open problem for generative models. This paper pushes the technical frontier of image outpainting in two directions that have not been resolved in literature: 1) outpainting with arbitrary and continuous multiples (without restriction), and 2) outpainting in a single step (even for large expansion multiples). Moreover, we develop a method that does not depend on a pre-trained backbone network, which is in contrast commonly required by the previous SOTA outpainting methods. The arbitrary multiple outpainting is achieved by utilizing randomly cropped views from the same image during training to capture arbitrary relative positional information. Specifically, by feeding one view and positional embeddings as queries, we can reconstruct another view. At inference, we generate images with arbitrary expansion multiples by inputting an anchor image and its corresponding positional embeddings. The one-step outpainting ability here is particularly noteworthy in contrast to previous methods that need to be performed for $N$ times to obtain a final multiple which is $N$ times of its basic and fixed multiple. We evaluate the proposed approach (called PQDiff as we adopt a diffusion-based generator as our embodiment, under our proposed \textbf{P}ositional \textbf{Q}uery scheme) on public benchmarks, demonstrating its superior performance over state-of-the-art approaches. Specifically, PQDiff achieves state-of-the-art FID scores on the Scenery (\textbf{21.512}), Building Facades (\textbf{25.310}), and WikiArts (\textbf{36.212}) datasets. Furthermore, under the 2.25x, 5x and 11.7x outpainting settings, PQDiff only takes \textbf{40.6\%}, \textbf{20.3\%} and \textbf{10.2\%} of the time of the benchmark state-of-the-art (SOTA) method. | https://openreview.net/pdf/e7c1ebdbe2aa6ff295ceeeda77b1ad0d61979895.pdf |
When Semantic Segmentation Meets Frequency Aliasing | https://openreview.net/forum?id=SYBdkHcXXK | https://openreview.net/forum?id=SYBdkHcXXK | Linwei Chen,Lin Gu,Ying Fu | ICLR 2024,Poster | Despite recent advancements in semantic segmentation, where and what pixels are hard to segment remains largely unexplored.
Existing research only separates an image into easy and hard regions and empirically observes the latter are associated with object boundaries.
In this paper, we conduct a comprehensive analysis of hard pixel errors, categorizing them into three types: false responses, merging mistakes, and displacements.
Our findings reveal a quantitative association between hard pixels and aliasing,
which is distortion caused by the overlapping of frequency components in the Fourier domain during downsampling.
To identify the frequencies responsible for aliasing, we propose using the equivalent sampling rate to calculate the Nyquist frequency, which marks the threshold for aliasing.
Then, we introduce the aliasing score as a metric to quantify the extent of aliasing.
While positively correlated with the proposed aliasing score, three types of hard pixels exhibit different patterns.
Here, we propose two novel de-aliasing filter (DAF) and frequency mixing (FreqMix) modules to alleviate aliasing degradation by accurately removing or adjusting frequencies higher than the Nyquist frequency.
The DAF precisely removes the frequencies responsible for aliasing before downsampling,
while the FreqMix dynamically selects high-frequency components within the encoder block.
Experimental results demonstrate consistent improvements in semantic segmentation and low-light instance segmentation tasks.
The code is at: \url{https://github.com/Linwei-Chen/Seg-Aliasing}. | https://openreview.net/pdf/44c5a71476bcaa674a34453d701fa62843aa6c7d.pdf |
Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models | https://openreview.net/forum?id=Od39h4XQ3Y | https://openreview.net/forum?id=Od39h4XQ3Y | Yili Wang,Kaixiong Zhou,Ninghao Liu,Ying Wang,Xin Wang | ICLR 2024,Poster | Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and mitigate generalization degradation. However, SAM requires two sequential gradient computations during the optimization of each step: one to obtain the perturbation gradient and the other to obtain the updating gradient.
Compared with the base optimizer (e.g., Adam), SAM doubles the time overhead due to the additional perturbation gradient. By dissecting the theory of SAM and observing the training gradient of the molecular graph transformer, we propose a new algorithm named GraphSAM, which reduces the training cost of SAM and improves the generalization performance of graph transformer models.
There are two key factors that contribute to this result: (i) \textit{gradient approximation}: we use the updating gradient of the previous step to approximate the perturbation gradient at the intermediate steps smoothly (\textbf{increases efficiency}); (ii) \textit{loss landscape approximation}: we theoretically prove that the loss landscape of GraphSAM is limited to a small range centered on the expected loss of SAM (\textbf{guarantees generalization performance}). The extensive experiments on six datasets with different tasks demonstrate the superiority of GraphSAM, especially in optimizing the model update process. | https://openreview.net/pdf/18456a69ecee34f68549d5dc59cbfb15f3b0ebd1.pdf |
MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo | https://openreview.net/forum?id=wXWfvSpYHh | https://openreview.net/forum?id=wXWfvSpYHh | Chenjie Cao,Xinlin Ren,Yanwei Fu | ICLR 2024,Poster | Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms. However, existing approaches have not thoroughly investigated the profound influence of transformers on different MVS modules, resulting in limited depth estimation capabilities. In this paper, we introduce MVSFormer++, a method that prudently maximizes the inherent characteristics of attention to enhance various components of the MVS pipeline. Formally, our approach involves infusing cross-view information into the pre-trained DINOv2 model to facilitate MVS learning. Furthermore, we employ different attention mechanisms for the feature encoder and cost volume regularization, focusing on feature and spatial aggregations respectively. Additionally, we uncover that some design details would substantially impact the performance of transformer modules in MVS, including normalized 3D positional encoding, adaptive attention scaling, and the position of layer normalization. Comprehensive experiments on DTU, Tanks-and-Temples, BlendedMVS, and ETH3D validate the effectiveness of the proposed method. Notably, MVSFormer++ achieves state-of-the-art performance on the challenging DTU and Tanks-and-Temples benchmarks. Codes and models are available at https://github.com/maybeLx/MVSFormerPlusPlus. | https://openreview.net/pdf/8d07314fc8d300ce740939d62a96c08aafa0dc87.pdf |
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation | https://openreview.net/forum?id=QyFm3D3Tzi | https://openreview.net/forum?id=QyFm3D3Tzi | Yuan Yuan,Chenyang Shao,Jingtao Ding,Depeng Jin,Yong Li | ICLR 2024,Poster | Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPD, for spatio-temporal few-shot learning with urban knowledge transfer. Unlike conventional approaches that heavily rely on common feature extraction or intricate few-shot learning designs, our solution takes a novel approach by performing generative pre-training on a collection of neural network parameters optimized with data from source cities. We recast spatio-temporal few-shot learning as pre-training a generative diffusion model, which generates tailored neural networks guided by prompts, allowing for adaptability to diverse data distributions and city-specific characteristics. GPD employs a Transformer-based denoising diffusion model, which is model-agnostic to integrate with powerful spatio-temporal neural networks. By addressing challenges arising from data gaps and the complexity of generalizing knowledge across cities, our framework consistently outperforms state-of-the-art baselines on multiple real-world datasets for tasks such as traffic speed prediction and crowd flow prediction. The implementation of our approach is available: https://github.com/tsinghua-fib-lab/GPD. | https://openreview.net/pdf/2e9ce7a1ca7531ead3955e5b46d9a21bcabf4a83.pdf |
Theoretical Understanding of Learning from Adversarial Perturbations | https://openreview.net/forum?id=Ww9rWUAcdo | https://openreview.net/forum?id=Ww9rWUAcdo | Soichiro Kumano,Hiroshi Kera,Toshihiko Yamasaki | ICLR 2024,Poster | It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that adversarial perturbations, while appearing as noises, contain class features. This is supported by empirical evidence showing that networks trained on mislabeled adversarial examples can still generalize well to correctly labeled test samples. However, a theoretical understanding of how perturbations include class features and contribute to generalization is limited. In this study, we provide a theoretical framework for understanding learning from perturbations using a one-hidden-layer network trained on mutually orthogonal samples. Our results highlight that various adversarial perturbations, even perturbations of a few pixels, contain sufficient class features for generalization. Moreover, we reveal that the decision boundary when learning from perturbations matches that from standard samples except for specific regions under mild conditions. The code is available at https://github.com/s-kumano/learning-from-adversarial-perturbations. | https://openreview.net/pdf/92249e84bdd727287ae719d37612b7a52afc6353.pdf |
Graph Lottery Ticket Automated | https://openreview.net/forum?id=nmBjBZoySX | https://openreview.net/forum?id=nmBjBZoySX | Guibin Zhang,Kun Wang,Wei Huang,Yanwei Yue,Yang Wang,Roger Zimmermann,Aojun Zhou,Dawei Cheng,Jin Zeng,Yuxuan Liang | ICLR 2024,Poster | Graph Neural Networks (GNNs) have emerged as the leading deep learning models for graph-based representation learning. However, the training and inference of GNNs on large graphs remain resource-intensive, impeding their utility in real-world scenarios and curtailing their applicability in deeper and more sophisticated GNN architectures. To address this issue, the Graph Lottery Ticket (GLT) hypothesis assumes that GNN with random initialization harbors a pair of core subgraph and sparse subnetwork, which can yield comparable performance and higher efficiency to that of the original dense network and complete graph. Despite that GLT offers a new paradigm for GNN training and inference, existing GLT algorithms heavily rely on trial-and-error pruning rate tuning and scheduling, and adhere to an irreversible pruning paradigm that lacks elasticity. Worse still, current methods suffer scalability issues when applied to deep GNNs, as they maintain the same topology structure across all layers. These challenges hinder the integration of GLT into deeper and larger-scale GNN contexts. To bridge this critical gap, this paper introduces an $\textbf{A}$daptive, $\textbf{D}$ynamic, and $\textbf{A}$utomated framework for identifying $\textbf{G}$raph $\textbf{L}$ottery $\textbf{T}$ickets ($\textbf{AdaGLT}$). Our proposed method derives its key advantages and addresses the above limitations through the following three aspects: 1) tailoring layer-adaptive sparse structures for various datasets and GNNs, thus endowing it with the capability to facilitate deeper GNNs; 2) integrating the pruning and training processes, thereby achieving a dynamic workflow encompassing both pruning and restoration; 3) automatically capturing graph lottery tickets across diverse sparsity levels, obviating the necessity for extensive pruning parameter tuning. More importantly, we rigorously provide theoretical proofs to guarantee $\textbf{AdaGLT}$ to mitigate over-smoothing issues and obtain improved sparse structures in deep GNN scenarios. Extensive experiments demonstrate that $\textbf{AdaGLT}$ outperforms state-of-the-art competitors across multiple graph datasets of various scales and types, particularly in scenarios involving deep GNNs. | https://openreview.net/pdf/7040512e6b7461e6e11298bf58b66e0595daec86.pdf |
FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling | https://openreview.net/forum?id=ijoqFqSC7p | https://openreview.net/forum?id=ijoqFqSC7p | Haonan Qiu,Menghan Xia,Yong Zhang,Yingqing He,Xintao Wang,Ying Shan,Ziwei Liu | ICLR 2024,Poster | With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress. However, existing video generation models are typically trained on a limited number of frames, resulting in the inability to generate high-fidelity long videos during inference. Furthermore, these models only support single-text conditions, whereas real-life scenarios often require multi-text conditions as the video content changes over time. To tackle these challenges, this study explores the potential of extending the text-driven capability to generate longer videos conditioned on multiple texts. 1) We first analyze the impact of initial noise in video diffusion models. Then building upon the observation of noise, we propose FreeNoise, a tuning-free and time-efficient paradigm to enhance the generative capabilities of pretrained video diffusion models while preserving content consistency. Specifically, instead of initializing noises for all frames, we reschedule a sequence of noises for long-range correlation and perform temporal attention over them by window-based fusion. 2) Additionally, we design a novel motion injection method to support the generation of videos conditioned on multiple text prompts. Extensive experiments validate the superiority of our paradigm in extending the generative capabilities of video diffusion models. It is noteworthy that compared with the previous best-performing method which brought about 255% extra time cost, our method incurs only negligible time cost of approximately 17%. Generated video samples are available at our website: http://haonanqiu.com/projects/FreeNoise.html. | https://openreview.net/pdf/bd47f35c18df619e675c737ccc56c1d802537b73.pdf |
Inner Classifier-Free Guidance and Its Taylor Expansion for Diffusion Models | https://openreview.net/forum?id=0QAzIMq32X | https://openreview.net/forum?id=0QAzIMq32X | Shikun Sun,Longhui Wei,Zhicai Wang,Zixuan Wang,Junliang Xing,Jia Jia,Qi Tian | ICLR 2024,Poster | Classifier-free guidance (CFG) is a pivotal technique for balancing the diversity and fidelity of samples in conditional diffusion models. This approach involves utilizing a single model to jointly optimize the conditional score predictor and unconditional score predictor, eliminating the need for additional classifiers. It delivers impressive results and can be employed for continuous and discrete condition representations. However, when the condition is continuous, it prompts the question of whether the trade-off can be further enhanced. Our proposed inner classifier-free guidance (ICFG) provides an alternative perspective on the CFG method when the condition has a specific structure, demonstrating that CFG represents a first-order case of ICFG. Additionally, we offer a second-order implementation, highlighting that even without altering the training policy, our second-order approach can introduce new valuable information and achieve an improved balance between fidelity and diversity for Stable Diffusion. | https://openreview.net/pdf/b8de189ef5b80efae6c27a1dfeadf5c968569827.pdf |
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process | https://openreview.net/forum?id=cMPm8YFXZe | https://openreview.net/forum?id=cMPm8YFXZe | Changyao Tian,Chenxin Tao,Jifeng Dai,Hao Li,Ziheng Li,Lewei Lu,Xiaogang Wang,Hongsheng Li,Gao Huang,Xizhou Zhu | ICLR 2024,Poster | Image recognition and generation have long been developed independently of each other. With the recent trend towards general-purpose representation learning, the development of general representations for both recognition and generation tasks is also promoted. However, preliminary attempts mainly focus on generation performance, but are still inferior on recognition tasks. These methods are modeled in the vector-quantized (VQ) space, whereas leading recognition methods use pixels as inputs. Our key insights are twofold: *(1) pixels as inputs are crucial for recognition tasks; (2) VQ tokens as reconstruction targets are beneficial for generation tasks.* These observations motivate us to propose an **Alternating Denoising Diffusion Process (ADDP)** that integrates these two spaces within a single representation learning framework. In each denoising step, our method first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels. The diffusion process gradually masks out a portion of VQ tokens to construct the training samples. The learned representations can be used to generate diverse high-fidelity images and also demonstrate excellent transfer performance on recognition tasks. Extensive experiments show that our method achieves competitive performance on unconditional generation, ImageNet classification, COCO detection, and ADE20k segmentation. Importantly, our method represents *the first successful development* of general representations applicable to both generation and dense recognition tasks. Code shall be released. | https://openreview.net/pdf/2adfc78f0e2c49b111fc83864808405f49504a51.pdf |
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks | https://openreview.net/forum?id=9nsNyN0vox | https://openreview.net/forum?id=9nsNyN0vox | Yixuan Weng,Minjun Zhu,Fei Xia,Bin Li,Shizhu He,Kang Liu,Jun Zhao | ICLR 2024,Poster | Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To endow LMs with genuine rule comprehension abilities, we propose "Neural Comprehension" - a framework that synergistically integrates compiled neural networks (CoNNs) into the standard transformer architecture. CoNNs are neural modules designed to explicitly encode rules through artificially generated attention weights. By incorporating CoNN modules, the Neural Comprehension framework enables LMs to accurately and robustly execute rule-intensive symbolic tasks. Extensive experiments demonstrate the superiority of our approach over existing techniques in terms of length generalization, efficiency, and interpretability for symbolic operations. Furthermore, it can be applied to LMs across different model scales, outperforming tool-calling methods in arithmetic reasoning tasks while maintaining superior inference efficiency. Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities. The code is released at: \url{https://github.com/wengsyx/Neural-Comprehension}. | https://openreview.net/pdf/88180280de07528f31bd901254b321280471c461.pdf |
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units | https://openreview.net/forum?id=oEF7qExD9F | https://openreview.net/forum?id=oEF7qExD9F | Zeyu Liu,Gourav Datta,Anni Li,Peter Anthony Beerel | ICLR 2024,Poster | Transformer models have demonstrated high accuracy in numerous applications but have high complexity and lack sequential processing capability making them ill-suited for many streaming applications at the edge where devices are heavily resource-constrained. Thus motivated, many researchers have proposed reformulating the transformer models as RNN modules which modify the self-attention computation with explicit states. However, these approaches often incur significant performance degradation.
The ultimate goal is to develop a model that has the following properties: parallel training, streaming and low-cost inference, and state-of-the-art (SOTA) performance. In this paper, we propose a new direction to achieve this goal. We show how architectural modifications to a fully-sequential recurrent model can help push its performance toward Transformer models while retaining its sequential processing capability. Specifically, inspired by the recent success of Legendre Memory Units (LMU) in sequence learning tasks, we propose LMUFormer, which augments the LMU with convolutional patch embedding and convolutional channel mixer.
Moreover, we present a spiking version of this architecture, which introduces the benefit of states within the patch embedding and channel mixer modules while simultaneously reducing the computing complexity.
We evaluated our architectures on multiple sequence datasets. Of particular note is our performance on the Speech Commands V2 dataset (35 classes). In comparison to SOTA transformer-based models within the ANN domain, our LMUFormer demonstrates comparable performance while necessitating a remarkable $70\times$ reduction in parameters and a substantial $140\times$ decrement in FLOPs. Furthermore, when benchmarked against extant low-complexity SNN variants, our model establishes a new SOTA with an accuracy of 96.12\%.
Additionally, owing to our model's proficiency in real-time data processing, we are able to achieve a 32.03\% reduction in sequence length, all while incurring an inconsequential decline in performance. | https://openreview.net/pdf/25038c1592ba0f911e023c687123225c5d89f08b.pdf |
InterpGNN: Understand and Improve Generalization Ability of Transdutive GNNs through the Lens of Interplay between Train and Test Nodes | https://openreview.net/forum?id=pwW807WJ9G | https://openreview.net/forum?id=pwW807WJ9G | Jiawei Sun,Kailai Li,Ruoxin Chen,Jie LI,Chentao Wu,Yue Ding,Junchi Yan | ICLR 2024,Poster | Transductive node prediction has been a popular learning setting in Graph Neural Networks (GNNs). It has been widely observed that the shortage of information flow between the distant nodes and intra-batch nodes (for large-scale graphs) often hurt the generalization of GNNs which overwhelmingly adopt message-passing. Yet there is still no formal and direct theoretical results to quantitatively capture the underlying mechanism, despite the recent advance in both theoretical and empirical studies for GNN's generalization ability. In this paper, the $L$-hop interplay (i.e., message passing capability with training nodes) for a $L$-layer GNN is successfully incorporated in our derived PAC-Bayesian bound for GNNs in the semi-supervised transductive setting. In other words, we quantitatively show how the interplay between training and testing sets influence the generalization ability which also partly explains the effectiveness of some existing empirical methods for enhancing generalization. Based on this result, we further design a plug-and-play ***Graph** **G**lobal **W**orkspace* module for GNNs (InterpGNN-GW) to enhance the interplay, utilizing the key-value attention mechanism to summarize crucial nodes' embeddings into memory and broadcast the memory to all nodes, in contrast to the pairwise attention scheme in previous graph transformers. Extensive experiments on both small-scale and large-scale graph datasets validate the effectiveness of our theory and approaches. | https://openreview.net/pdf/4441b0fe7dd9510db98ac6bbb52a923f4d59385a.pdf |
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction | https://openreview.net/forum?id=6iwg437CZs | https://openreview.net/forum?id=6iwg437CZs | Dennis Wu,Jerry Yao-Chieh Hu,Weijian Li,Bo-Yu Chen,Han Liu | ICLR 2024,Poster | We present **STanHop-Net** (**S**parse **Tan**dem **Hop**field **Net**work) for multivariate time series prediction with memory-enhanced capabilities. At the heart of our approach is **STanHop**, a novel Hopfield-based neural network block, which sparsely learns and stores both temporal and cross-series representations in a data-dependent fashion. In essence, STanHop sequentially learns temporal representation and cross-series representation using two tandem sparse Hopfield layers. Additionally, STanHop incorporates two external memory modules: **Plug-and-Play** and **Tune-and-Play** for train-less and task-aware memory enhancements, respectively. They allow StanHop-Net to swiftly respond to sudden events. Methodologically, we construct the STanHop-Net by stacking STanHop blocks in a hierarchical fashion, enabling multi-resolution feature extraction with resolution-specific sparsity. Theoretically, we introduce a unified construction (**Generalized Sparse Modern Hopfield Model**) for both dense and sparse modern Hopfield models and show that it endows a tighter memory retrieval error compared to the dense counterpart without sacrificing memory capacity. Empirically, we validate the efficacy of STanHop-Net on many settings: time series prediction, fast test-time adaptation, and strongly correlated time series prediction. | https://openreview.net/pdf/a9fd7a9f51f5efc7483097287fe1713e609f614e.pdf |
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images | https://openreview.net/forum?id=BteuUysuXX | https://openreview.net/forum?id=BteuUysuXX | Kuofeng Gao,Yang Bai,Jindong Gu,Shu-Tao Xia,Philip Torr,Zhifeng Li,Wei Liu | ICLR 2024,Poster | Large vision-language models (VLMs) such as GPT-4 have achieved exceptional performance across various multi-modal tasks. However, the deployment of VLMs necessitates substantial energy consumption and computational resources. Once attackers maliciously induce high energy consumption and latency time (energy-latency cost) during inference of VLMs, it will exhaust computational resources. In this paper, we explore this attack surface about availability of VLMs and aim to induce high energy-latency cost during inference of VLMs. We find that high energy-latency cost during inference of VLMs can be manipulated by maximizing the length of generated sequences. To this end, we propose verbose images, with the goal of crafting an imperceptible perturbation to induce VLMs to generate long sentences during inference. Concretely, we design three loss objectives. First, a loss is proposed to delay the occurrence of end-of-sequence (EOS) token, where EOS token is a signal for VLMs to stop generating further tokens. Moreover, an uncertainty loss and a token diversity loss are proposed to increase the uncertainty over each generated token and the diversity among all tokens of the whole generated sequence, respectively, which can break output dependency at token-level and sequence-level. Furthermore, a temporal weight adjustment algorithm is proposed, which can effectively balance these losses. Extensive experiments demonstrate that our verbose images can increase the length of generated sequences by 7.87× and 8.56× compared to original images on MS-COCO and ImageNet datasets, which presents potential challenges for various applications. | https://openreview.net/pdf/803c585a3244829aa3083b6aa8aef4fb0f49d3e7.pdf |
Progressive Fourier Neural Representation for Sequential Video Compilation | https://openreview.net/forum?id=rGFrRMBbOq | https://openreview.net/forum?id=rGFrRMBbOq | Haeyong Kang,Jaehong Yoon,DaHyun Kim,Sung Ju Hwang,Chang D. Yoo | ICLR 2024,Poster | Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space and easily reconstruct it through a trainable mapping function. However, NIR methods assume a one-to-one mapping between the target data and representation models regardless of data relevancy or similarity. This results in poor generalization over multiple complex data and limits their efficiency and scalability. Motivated by continual learning, this work investigates how to accumulate and transfer neural implicit representations for multiple complex video data over sequential encoding sessions. To overcome the limitation of NIR, we propose a novel method, Progressive Fourier Neural Representation (PFNR), that aims to find an adaptive and compact sub-module in Fourier space to encode videos in each training session. This sparsified neural encoding allows the neural network to hold free weights, enabling an improved adaptation for future videos. In addition, when learning a representation for a new video, PFNR transfers the representation of previous videos with frozen weights. This design allows the model to continuously accumulate high-quality neural representations for multiple videos while ensuring lossless decoding that perfectly preserves the learned representations for previous videos. We validate our PFNR method on the UVG8/17 and DAVIS50 video sequence benchmarks and achieve impressive performance gains over strong continual learning baselines. | https://openreview.net/pdf/513db12761e61eb7fdc7d0286f72e7090a8945f7.pdf |
Adaptive deep spiking neural network with global-local learning via balanced excitatory and inhibitory mechanism | https://openreview.net/forum?id=wpnlc2ONu0 | https://openreview.net/forum?id=wpnlc2ONu0 | Tingting Jiang,Qi Xu,Xuming Ran,Jiangrong Shen,Pan Lv,Qiang Zhang,Gang Pan | ICLR 2024,Poster | The training method of Spiking Neural Networks (SNNs) is an essential problem, and how to integrate local and global learning is a worthy research interest. However, the current integration methods do not consider the network conditions suitable for local and global learning, and thus fail to balance their advantages. In this paper, we propose an Excitation-Inhibition Mechanism-assisted Hybrid Learning(EIHL) algorithm that adjusts the network connectivity by using the excitation-inhibition mechanism and then switches between local and global learning according to the network connectivity. The experimental results on CIFAR10/100 and DVS-CIFAR10 demonstrate that the EIHL not only has better accuracy performance than other methods but also has excellent sparsity advantage. Especially, the Spiking VGG11 is trained by EIHL, STBP, and STDP on DVS_CIFAR10, respectively. The accuracy of the Spiking VGG11 model on EIHL is 62.45%, which is 4.35% higher than STBP and 11.40% higher than STDP, and the sparsity is 18.74%, which is 18.74% higher than the other two methods. Moreover, the excitation-inhibition mechanism used in our method also offers a new perspective on the field of SNN learning. | https://openreview.net/pdf/c5b667eeab004e67b805d67a029a04ee48ce9a4e.pdf |
Diffusion Posterior Sampling for Linear Inverse Problem Solving: A Filtering Perspective | https://openreview.net/forum?id=tplXNcHZs1 | https://openreview.net/forum?id=tplXNcHZs1 | Zehao Dou,Yang Song | ICLR 2024,Poster | Diffusion models have achieved tremendous success in generating high-dimensional data like images, videos and audio. These models provide powerful data priors that can solve linear inverse problems in zero shot through Bayesian posterior sampling.
However, exact posterior sampling for diffusion models is intractable. Current solutions often hinge on approximations that are either computationally expensive or lack strong theoretical guarantees. In this work, we introduce an efficient diffusion sampling algorithm for linear inverse problems that is guaranteed to be asymptotically accurate. We reveal a link between Bayesian posterior sampling and Bayesian filtering in diffusion models, proving the former as a specific instance of the latter. Our method, termed filtering posterior sampling, leverages sequential Monte Carlo methods to solve the corresponding filtering problem. It seamlessly integrates with all Markovian diffusion samplers, requires no model re-training, and guarantees accurate samples from the Bayesian posterior as particle counts rise. Empirical tests demonstrate that our method generates better or comparable results than leading zero-shot diffusion posterior samplers on tasks like image inpainting, super-resolution, and deblurring. | https://openreview.net/pdf/aa6810130f2e3135146ade683b52798319e1af04.pdf |
How connectivity structure shapes rich and lazy learning in neural circuits | https://openreview.net/forum?id=slSmYGc8ee | https://openreview.net/forum?id=slSmYGc8ee | Yuhan Helena Liu,Aristide Baratin,Jonathan Cornford,Stefan Mihalas,Eric Todd SheaBrown,Guillaume Lajoie | ICLR 2024,Poster | In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity generally has a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights — in particular their effective rank — influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting. | https://openreview.net/pdf/fda2947f75642f74e4c12e1a8f94ca9e8c99496b.pdf |
An LLM can Fool Itself: A Prompt-Based Adversarial Attack | https://openreview.net/forum?id=VVgGbB9TNV | https://openreview.net/forum?id=VVgGbB9TNV | Xilie Xu,Keyi Kong,Ning Liu,Lizhen Cui,Di Wang,Jingfeng Zhang,Mohan Kankanhalli | ICLR 2024,Poster | The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM’s adversarial robustness. This paper proposes an efficient tool to audit the LLM’s adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. The attack prompt is composed of three important components: (1) original input (OI) including the original sample and its ground-truth label, (2) attack objective (AO) illustrating a task description of generating a new sample that can fool itself without changing the semantic meaning, and (3) attack guidance (AG) containing the perturbation instructions to guide the LLM on how to complete the task by perturbing the original sample at character, word, and sentence levels, respectively. Besides, we use a fidelity filter to ensure that PromptAttack maintains the original semantic meanings of the adversarial examples. Further, we enhance the attack power of PromptAttack by ensembling adversarial examples at different perturbation levels. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++. Interesting findings include that a simple emoji can easily mislead GPT-3.5 to make wrong predictions. Our source code is available at https://github.com/GodXuxilie/PromptAttack. | https://openreview.net/pdf/ba23546abb3c1cd83f22d6160f328c40fdadc123.pdf |
AutoLoRa: An Automated Robust Fine-Tuning Framework | https://openreview.net/forum?id=09xFexjhqE | https://openreview.net/forum?id=09xFexjhqE | Xilie Xu,Jingfeng Zhang,Mohan Kankanhalli | ICLR 2024,Poster | Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial robustness in downstream applications, without requiring a lot of computational resources and collecting significant amounts of data. This paper uncovers an issue with the existing RFT,
where optimizing both adversarial and natural objectives through the feature extractor (FE) yields significantly divergent gradient directions. This divergence introduces instability in the optimization process, thereby hindering the attainment of adversarial robustness and rendering RFT highly sensitive to hyperparameters. To mitigate this issue, we propose a low-rank (LoRa) branch that disentangles RFT into two distinct components: optimizing natural objectives via the LoRa branch and adversarial objectives via the FE. Besides, we introduce heuristic strategies for automating the scheduling of the learning rate and the scalars of loss terms. Extensive empirical evaluations demonstrate that our proposed automated RFT disentangled via the LoRa branch (AutoLoRa) achieves new state-of-the-art results across a range of downstream tasks. AutoLoRa holds significant practical utility, as it automatically converts a pre-trained FE into an adversarially robust model for downstream tasks without the need for searching hyperparameters. Our source code is available at [the GitHub](https://github.com/GodXuxilie/RobustSSL_Benchmark/tree/main/Finetuning_Methods/AutoLoRa). | https://openreview.net/pdf/0782024f39ecc4e566699081da1eebc5e47ea399.pdf |
Denoising Diffusion Step-aware Models | https://openreview.net/forum?id=c43FGk8Pcg | https://openreview.net/forum?id=c43FGk8Pcg | Shuai Yang,Yukang Chen,Luozhou Wang,Shu Liu,Ying-Cong Chen | ICLR 2024,Poster | Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains. However, a significant bottleneck is the necessity for whole-network computation during every step of the generative process, leading to high computational overheads. This paper presents a novel framework, Denoising Diffusion Step-aware Models (DDSM), to address this challenge. Unlike conventional approaches, DDSM employs a spectrum of neural networks whose sizes are adapted according to the importance of each generative step, as determined through evolutionary search. This step-wise network variation effectively circumvents redundant computational efforts, particularly in less critical steps, thereby enhancing the efficiency of the diffusion model. Furthermore, the step-aware design can be seamlessly integrated with other efficiency-geared diffusion models such as DDIMs and latent diffusion, thus broadening the scope of computational savings. Empirical evaluations demonstrate that DDSM achieves computational savings of 49% for CIFAR-10, 61% for CelebA-HQ, 59% for LSUN-bedroom, 71% for AFHQ, and 76% for ImageNet, all without compromising the generation quality. Our code and models are available at https://github.com/EnVision-Research/DDSM. | https://openreview.net/pdf/be6a50bc1a0d44b5e3c5f9356a06c85d05d092c8.pdf |
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs | https://openreview.net/forum?id=QmYNBVukex | https://openreview.net/forum?id=QmYNBVukex | Feiyang Kang,Hoang Anh Just,Yifan Sun,Himanshu Jahagirdar,Yuanzhi Zhang,Rongxing Du,Anit Kumar Sahu,Ruoxi Jia | ICLR 2024,Poster | This work focuses on leveraging and selecting from vast, unlabeled, open data to *pre-fine-tune* a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms have been designed for small-scale applications, rendering them unsuitable for our context, some emerging methods do cater to language data scales. However, they often prioritize data that aligns with the target distribution. While this strategy may be effective when training a model from scratch, it can yield limited results when the model has already been pre-trained on a different distribution. Differing from prior work, our key idea is to select data that nudges the pre-training distribution closer to the target distribution. We show the optimality of this approach for fine-tuning tasks under certain conditions. We demonstrate the efficacy of our methodology across a diverse array of tasks (NLU, NLG, zero-shot) with models up to 2.7B, showing that it consistently surpasses other selection methods. Moreover, our proposed method is significantly faster than existing techniques, scaling to millions of samples within a single GPU hour. Our code is open-sourced. While fine-tuning offers significant potential for enhancing performance across diverse tasks, its associated costs often limit its widespread adoption; with this work, we hope to lay the groundwork for cost-effective fine-tuning, making its benefits more accessible. | https://openreview.net/pdf/928e061604b47e67e114e257c465db4986ecc0be.pdf |
3D Reconstruction with Generalizable Neural Fields using Scene Priors | https://openreview.net/forum?id=Nu7dDaVF5a | https://openreview.net/forum?id=Nu7dDaVF5a | Yang Fu,Shalini De Mello,Xueting Li,Amey Kulkarni,Jan Kautz,Xiaolong Wang,Sifei Liu | ICLR 2024,Poster | High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-view stereo methods alleviate this issue to some extent, their multi-view setting makes it less flexible to scale up and to broad applications. Instead, we introduce training generalizable Neural Fields incorporating scene Priors (NFPs).
The NFP network maps any single-view RGB-D image into signed distance and radiance values. A complete scene can be reconstructed by merging individual frames in the volumetric space WITHOUT a fusion module, which provides better flexibility. The scene priors can be trained on large-scale datasets, allowing for fast adaptation to the reconstruction of a new scene with fewer views. NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is under-explored in neural fields. More qualitative results are available at: https://oasisyang.github.io/neural-prior. | https://openreview.net/pdf/dbbcafaf634839bc2a3088e06fcca4615c84d362.pdf |
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions | https://openreview.net/forum?id=MukGKGtgnr | https://openreview.net/forum?id=MukGKGtgnr | Xiu-Chuan Li,Kun Zhang,Tongliang Liu | ICLR 2024,Poster | Traditional causal discovery approaches typically assume the absence of latent variables, a simplification that often does not align with real-world situations. Recently, there has been a surge of causal discovery methods that explicitly consider latent variables. While some works aim to reveal causal relations between observed variables in the presence of latent variables, others seek to identify latent variables and recover the causal structure over them. The latter typically entail strong distributional and graphical assumptions, such as the non-Gaussianity, purity, and two-pure-children assumption. In this paper, we endeavor to recover the whole causal structure involving both latent and observed variables under milder assumptions. We formulate two cases, one allows entirely arbitrary distribution and requires only one pure child per latent variable, and the other requires no pure child and imposes the non-Gaussianity requirement on only a subset of variables, and they both avoid the purity assumption. We prove the identifiability of linear latent variable models in both cases, and our constructive proof leads to theoretically sound and computationally efficient algorithms. | https://openreview.net/pdf/9aaac876559e1922df64ec9bf1b7085bbdcc819a.pdf |
AutoVP: An Automated Visual Prompting Framework and Benchmark | https://openreview.net/forum?id=wR9qVlPh0P | https://openreview.net/forum?id=wR9qVlPh0P | Hsi-Ai Tsao,Lei Hsiung,Pin-Yu Chen,Sijia Liu,Tsung-Yi Ho | ICLR 2024,Poster | Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks. However, there has hitherto been little systematic study of the design space of VP and no clear benchmark for evaluating its performance. To bridge this gap, we propose AutoVP, an end-to-end expandable framework for automating VP design choices, along with 12 downstream image-classification tasks that can serve as a holistic VP-performance benchmark. Our design space covers 1) the joint optimization of the prompts; 2) the selection of pre-trained models, including image classifiers and text-image encoders; and 3) model output mapping strategies, including nonparametric and trainable label mapping. Our extensive experimental results show that AutoVP outperforms the best-known current VP methods by a substantial margin, having up to 6.7% improvement in accuracy; and attains a maximum performance increase of 27.5% compared to linear-probing (LP) baseline. AutoVP thus makes a two-fold contribution: serving both as an efficient tool for hyperparameter tuning on VP design choices, and as a comprehensive benchmark that can reasonably be expected to accelerate VP’s development. The source code is available at https://github.com/IBM/AutoVP. | https://openreview.net/pdf/c81a962ad964c657d9ad76776bfc1208028cc6b1.pdf |
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding | https://openreview.net/forum?id=5dlfiJIXoh | https://openreview.net/forum?id=5dlfiJIXoh | Yuanhao Xiong,Long Zhao,Boqing Gong,Ming-Hsuan Yang,Florian Schroff,Ting Liu,Cho-Jui Hsieh,Liangzhe Yuan | ICLR 2024,Poster | Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. A powerful model is expected to be capable of capturing region-object correspondences and recognizing scene changes in a video clip, reflecting spatial and temporal granularity, respectively. To strengthen model's understanding into such fine-grained details, we propose a simple yet effective video-language modeling framework, S-ViLM, by exploiting the intrinsic structures of these two modalities. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features, simultaneously. Comprehensive evaluations demonstrate that S-ViLM performs favorably against existing approaches in learning more expressive representations. Specifically, S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks, covering text-video retrieval, video question answering, video action recognition, and temporal action localization. | https://openreview.net/pdf/614ef089f0433f5ee67eeda2476978dcc525fb45.pdf |
WizardCoder: Empowering Code Large Language Models with Evol-Instruct | https://openreview.net/forum?id=UnUwSIgK5W | https://openreview.net/forum?id=UnUwSIgK5W | Ziyang Luo,Can Xu,Pu Zhao,Qingfeng Sun,Xiubo Geng,Wenxiang Hu,Chongyang Tao,Jing Ma,Qingwei Lin,Daxin Jiang | ICLR 2024,Poster | Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated remarkable performance in various code-related tasks. However, different from their counterparts in the general language modeling field, the technique of instruction fine-tuning remains relatively under-researched in this domain. In this paper, we present Code Evol-Instruct, a novel approach that adapts the Evol-Instruct method to the realm of code, enhancing Code LLMs to create novel models, WizardCoder. Through comprehensive experiments on five prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, DS-1000, and MultiPL-E, our models showcase outstanding performance. They consistently outperform all other open-source Code LLMs by a significant margin. Remarkably, WizardCoder 15B even surpasses the well-known closed-source LLMs, including Anthropic's Claude and Google's Bard, on the HumanEval and HumanEval+ benchmarks. Additionally, WizardCoder 34B not only achieves a HumanEval score comparable to GPT3.5 (ChatGPT) but also surpasses it on the HumanEval+ benchmark. Furthermore, our preliminary exploration highlights the pivotal role of instruction complexity in achieving exceptional coding performance. | https://openreview.net/pdf/25665bdf1aff093dd1043608a5801dee1e12c99f.pdf |
Order-Preserving GFlowNets | https://openreview.net/forum?id=VXDPXuq4oG | https://openreview.net/forum?id=VXDPXuq4oG | Yihang Chen,Lukas Mauch | ICLR 2024,Poster | Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates with probabilities proportional to a given reward. However, GFlowNets can only be used with a predefined scalar reward, which can be either computationally expensive or not directly accessible, in the case of multi-objective optimization (MOO) tasks for example. Moreover, to prioritize identifying high-reward candidates, the conventional practice is to raise the reward to a higher exponent, the optimal choice of which may vary across different environments. To address these issues, we propose Order-Preserving GFlowNets (OP-GFNs), which sample with probabilities in proportion to a learned reward function that is consistent with a provided (partial) order on the candidates, thus eliminating the need for an explicit formulation of the reward function. We theoretically prove that the training process of OP-GFNs gradually sparsifies the learned reward landscape in single-objective maximization tasks. The sparsification concentrates on candidates of a higher hierarchy in the ordering, ensuring exploration at the beginning and exploitation towards the end of the training. We demonstrate OP-GFN's state-of-the-art performance in single-objective maximization (totally ordered) and multi-objective Pareto front approximation (partially ordered) tasks, including synthetic datasets, molecule generation, and neural architecture search. | https://openreview.net/pdf/7db994e2b6453eeefbbd759a3887758bb0241f5f.pdf |
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs | https://openreview.net/forum?id=h6Tz85BqRI | https://openreview.net/forum?id=h6Tz85BqRI | Ling Yang,Ye Tian,Minkai Xu,Zhongyi Liu,Shenda Hong,Wei Qu,Wentao Zhang,Bin CUI,Muhan Zhang,Jure Leskovec | ICLR 2024,Poster | GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output representations of teacher GNN. Existing methods mainly make the MLP to mimic the GNN predictions over a few class labels. However, the class space may not be expressive enough for covering numerous diverse local graph structures, thus limiting the performance of knowledge transfer from GNN to MLP. To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation. Specifically, we propose a variant of VQ-VAE to learn a structure-aware tokenizer on graph data that can encode each node's local substructure as a discrete code. The discrete codes constitute a codebook as a new graph representation space that is able to identify different local graph structures of nodes with the corresponding code indices. Then, based on the learned codebook, we propose a new distillation target, namely soft code assignments, to directly transfer the structural knowledge of each node from GNN to MLP. The resulting framework VQGraph achieves new state-of-the-art performance on GNN-to-MLP distillation in both transductive and inductive settings across seven graph datasets. We show that VQGraph with better performance infers faster than GNNs by 828×, and also achieves accuracy improvement over GNNs and stand-alone MLPs by 3.90% and 28.05% on average, respectively. Our code is available at https://github.com/YangLing0818/VQGraph | https://openreview.net/pdf/982b86670cbbd7d7ea6672cc86dbd433e3e2a453.pdf |
Dual-Encoders for Extreme Multi-label Classification | https://openreview.net/forum?id=dNe1T0Ahby | https://openreview.net/forum?id=dNe1T0Ahby | Nilesh Gupta,Fnu Devvrit,Ankit Singh Rawat,Srinadh Bhojanapalli,Prateek Jain,Inderjit S Dhillon | ICLR 2024,Poster | Dual-encoder (DE) models are widely used in retrieval tasks, most commonly studied on open QA benchmarks that are often characterized by multi-class and limited training data. In contrast, their performance in multi-label and data-rich retrieval settings like extreme multi-label classification (XMC), remains under-explored. Current empirical evidence indicates that DE models fall significantly short on XMC benchmarks, where SOTA methods linearly scale the number of learnable parameters with the total number of classes (documents in the corpus) by employing per-class classification head. To this end, we first study and highlight that existing multi-label contrastive training losses are not appropriate for training DE models on XMC tasks. We propose decoupled softmax loss - a simple modification to the InfoNCE loss - that overcomes the limitations of existing contrastive losses. We further extend our loss design to a soft top-k operator-based loss which is tailored to optimize top-k prediction performance. When trained with our proposed loss functions, standard DE models alone can match or outperform SOTA methods by up to 2\% at Precision@1 even on the largest XMC datasets while being 20× smaller in terms of the number of trainable parameters. This leads to more parameter-efficient and universally applicable solutions for retrieval tasks. Our code and models are publicly available [here](https://github.com/nilesh2797/dexml). | https://openreview.net/pdf/eaf1c911fcab02c4015bb2062c446fc0c0e63cdf.pdf |
FasterViT: Fast Vision Transformers with Hierarchical Attention | https://openreview.net/forum?id=kB4yBiNmXX | https://openreview.net/forum?id=kB4yBiNmXX | Ali Hatamizadeh,Greg Heinrich,Hongxu Yin,Andrew Tao,Jose M. Alvarez,Jan Kautz,Pavlo Molchanov | ICLR 2024,Poster | We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs. We benefit from efficient window-based self-attention. Each window has access to dedicated carrier tokens that participate in local and global representation learning. At a high level, global self-attentions enable the efficient cross-window communication at lower costs. FasterViT achieves a SOTA Pareto-front in terms of accuracy and image throughput. We have extensively validated its effectiveness on various CV tasks including classification, object detection and segmentation. We also show that HAT can be used as a plug-and-play module for existing networks and enhance them. We further demonstrate significantly faster and more accurate performance than competitive counterparts for images with high resolution. Code is available at https://github.com/NVlabs/FasterViT. | https://openreview.net/pdf/2720a7fad29ce6f2f902a30d843ed8f36837cbe1.pdf |
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval | https://openreview.net/forum?id=COYDmKkQH4 | https://openreview.net/forum?id=COYDmKkQH4 | Qi Yan,Raihan Seraj,Jiawei He,Lili Meng,Tristan Sylvain | ICLR 2024,Poster | Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making. Whereas traditional forecasting predominantly hinges on structured data like time-series, recent breakthroughs in language models enable predictions using unstructured text. In particular, (Zou et al., 2022) unveils AutoCast, a new benchmark that employs news articles for answering forecasting queries. Nevertheless, existing methods still trail behind human performance. The cornerstone of accurate forecasting, we argue, lies in identifying a concise, yet rich subset of news snippets from a vast corpus. With this motivation, we introduce AutoCast++, a zero-shot ranking-based context retrieval system, tailored to sift through expansive news document collections for event forecasting. Our approach first re-ranks articles based on zero-shot question-passage relevance, honing in on semantically pertinent news. Following this, the chosen articles are subjected to zero-shot summarization to attain succinct context. Leveraging a pre-trained language model, we conduct both the relevance evaluation and article summarization without needing domain-specific training. Notably, recent articles can sometimes be at odds with preceding ones due to new facts or unanticipated incidents, leading to fluctuating temporal dynamics. To tackle this, our re-ranking mechanism gives preference to more recent articles, and we further regularize the multi-passage representation learning to align with human forecaster responses made on different dates. Empirical results underscore marked improvements across multiple metrics, improving the performance for multiple-choice questions (MCQ) by 48% and true/false (TF) questions by up to 8%. Code is available at
https://github.com/BorealisAI/Autocast-plus-plus. | https://openreview.net/pdf/b67112bce40646098831e6264ccaee229c495bed.pdf |
Feature Collapse | https://openreview.net/forum?id=gctmyMiPHH | https://openreview.net/forum?id=gctmyMiPHH | Thomas Laurent,James von Brecht,Xavier Bresson | ICLR 2024,Poster | We formalize and study a phenomenon called *feature collapse* that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations. As feature collapse requires a notion of task, we leverage a synthetic task in which a learner must classify `sentences' constituted of $L$ tokens. We start by showing experimentally that feature collapse goes hand in hand with generalization. We then prove that, in the large sample limit, distinct tokens that play identical roles in the task receive identical local feature representations in the first layer of the network. This analysis shows that a neural network trained on this task provably learns interpretable and meaningful representations in its first layer. | https://openreview.net/pdf/a0cce1aa4b612c7319f3c67cc9659b27b469a8a3.pdf |
Function Vectors in Large Language Models | https://openreview.net/forum?id=AwyxtyMwaG | https://openreview.net/forum?id=AwyxtyMwaG | Eric Todd,Millicent Li,Arnab Sen Sharma,Aaron Mueller,Byron C Wallace,David Bau | ICLR 2024,Poster | We report the presence of a simple neural mechanism that represents an input-output function as a vector within autoregressive transformer language models (LMs). Using causal mediation analysis on a diverse range of in-context-learning (ICL) tasks, we find that a small number attention heads transport a compact representation of the demonstrated task, which we call a function vector (FV). FVs are robust to changes in context, i.e., they trigger execution of the task on inputs such as zero-shot and natural text settings that do not resemble the ICL contexts from which they are collected. We test FVs across a range of tasks, models, and layers and find strong causal effects across settings in middle layers. We investigate the internal structure of FVs and find while that they often contain information that encodes the output space of the function, this information alone is not sufficient to reconstruct an FV. Finally, we test semantic vector composition in FVs, and find that to some extent they can be summed to create vectors that trigger new complex tasks. Our findings show that compact, causal internal vector representations of function abstractions can be explicitly extracted from LLMs. | https://openreview.net/pdf/176fc59da520f0197e0e7e51d6afe7563d11bfcc.pdf |
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking | https://openreview.net/forum?id=8sKcAWOf2D | https://openreview.net/forum?id=8sKcAWOf2D | Nikhil Prakash,Tamar Rott Shaham,Tal Haklay,Yonatan Belinkov,David Bau | ICLR 2024,Poster | Fine-tuning on generalized tasks such as instruction following, code generation, and mathematics has been shown to enhance language models' performance on a range of tasks. Nevertheless, explanations of how such fine-tuning influences the internal computations in these models remain elusive. We study how fine-tuning affects the internal mechanisms implemented in language models. As a case study, we explore the property of entity tracking, a crucial facet of language comprehension, where models fine-tuned on mathematics have substantial performance gains. We identify a mechanism that enables entity tracking and show that (i) both the original model and its fine-tuned version implement entity tracking with the same circuit. In fact, the entity tracking circuit of the fine-tuned version performs better than the full original model. (ii) The circuits of all the models implement roughly the same functionality, that is entity tracking is performed by tracking the position of the correct entity in both the original model and its fine-tuned version. (iii) Performance boost in the fine-tuned model is primarily attributed to its improved ability to handle positional information. To uncover these findings, we employ two methods: DCM, which automatically detects model components responsible for specific semantics, and CMAP, a new approach for patching activations across models to reveal improved mechanisms. Our findings suggest that fine-tuning enhances, rather than fundamentally alters, the mechanistic operation of the model. | https://openreview.net/pdf/a8f8ab6a74f280abed0a6a26d4afebdc11ad719a.pdf |
Perceptual Group Tokenizer: Building Perception with Iterative Grouping | https://openreview.net/forum?id=NnYaYVODyV | https://openreview.net/forum?id=NnYaYVODyV | Zhiwei Deng,Ting Chen,Yang Li | ICLR 2024,Poster | Human visual recognition system shows astonishing capability of compressing visual information into a set of tokens containing rich representations without label supervision. One critical driving principle behind it is perceptual grouping. Despite being widely used in computer vision in the early 2010s, it remains a mystery whether perceptual grouping can be leveraged to derive a neural visual recognition backbone that generates as powerful representations. In this paper, we propose the Perceptual Group Tokenizer, a model that entirely relies on grouping operations to extract visual features and perform self-supervised representation learning, where a series of grouping operations are used to iteratively hypothesize the context for pixels or superpixels to refine feature representations. We show that the proposed model can achieve competitive performance compared to state-of-the-art vision architectures, and inherits desirable properties including adaptive computation without re-training, and interpretability. Specifically, Perceptual Group Tokenizer achieves 79.7% on ImageNet-1K self-supervised learning benchmark with linear probe evaluation, marking a new progress under this paradigm. | https://openreview.net/pdf/897b727eba04627ab22b9ea0914da622a5d26876.pdf |
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms | https://openreview.net/forum?id=VTYg5ykEGS | https://openreview.net/forum?id=VTYg5ykEGS | William Yang,Byron Zhang,Olga Russakovsky | ICLR 2024,Poster | The task of out-of-distribution (OOD) detection is notoriously ill-defined. Earlier works focused on new-class detection, aiming to identify label-altering data distribution shifts, also known as "semantic shift." However, recent works argue for a focus on failure detection, expanding the OOD evaluation framework to account for label-preserving data distribution shifts, also known as "covariate shift.” Intriguingly, under this new framework, complex OOD detectors that were previously considered state-of-the-art now perform similarly to, or even worse than the simple maximum softmax probability baseline. This raises the question: what are the latest OOD detectors actually detecting? Deciphering the behavior of OOD detection algorithms requires evaluation datasets that decouples semantic shift and covariate shift. To aid our investigations, we present ImageNet-OOD, a clean semantic shift dataset that minimizes the interference of covariate shift. Through comprehensive experiments, we show that OOD detectors are more sensitive to covariate shift than to semantic shift, and the benefits of recent OOD detection algorithms on semantic shift detection is minimal. Our dataset and analyses provide important insights for guiding the design of future OOD detectors. | https://openreview.net/pdf/2e059357faf2ef261bbdf07fc0f6c695e33c30ca.pdf |
Self-supervised Representation Learning from Random Data Projectors | https://openreview.net/forum?id=EpYnZpDpsQ | https://openreview.net/forum?id=EpYnZpDpsQ | Yi Sui,Tongzi Wu,Jesse C. Cresswell,Ga Wu,George Stein,Xiao Shi Huang,Xiaochen Zhang,Maksims Volkovs | ICLR 2024,Poster | Self-supervised representation learning (SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data modalities, and can conflict with application-specific data augmentation constraints. This paper presents an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking. Specifically, we show that high-quality data representations can be learned by reconstructing random data projections. We evaluate the proposed approach on a wide range of representation learning tasks that span diverse modalities and real-world applications. We show that it outperforms multiple state-of-the-art SSRL baselines.
Due to its wide applicability and strong empirical results, we argue that learning from randomness is a fruitful research direction worthy of attention and further study. | https://openreview.net/pdf/33060b7350e6533192256b0ba8ffd4b961ede09d.pdf |
Approximately Piecewise E(3) Equivariant Point Networks | https://openreview.net/forum?id=aKJEHWmBEf | https://openreview.net/forum?id=aKJEHWmBEf | Matan Atzmon,Jiahui Huang,Francis Williams,Or Litany | ICLR 2024,Poster | Integrating a notion of symmetry into point cloud neural networks is a provably effective way to improve their generalization capability. Of particular interest are $E(3)$ equivariant point cloud networks where Euclidean transformations applied to the inputs are preserved in the outputs. Recent efforts aim to extend networks that are equivariant with respect to a single global $E(3)$ transformation, to accommodate inputs made of multiple parts, each of which exhibits local $E(3)$ symmetry.
In practical settings, however, the partitioning into individually transforming regions is unknown a priori.
Errors in the partition prediction would unavoidably map to errors in respecting the true input symmetry. Past works have proposed different ways to predict the partition, which may exhibit uncontrolled errors in their ability to maintain equivariance to the actual partition. To this end, we introduce APEN: a general framework for constructing approximate piecewise-$E(3)$ equivariant point networks. Our framework offers an adaptable design to guaranteed bounds on the resulting piecewise $E(3)$ equivariance approximation errors.
Our primary insight is that functions which are equivariant with respect to a finer partition (compared to the unknown true partition) will also maintain equivariance in relation to the true partition. Leveraging this observation, we propose a compositional design for a partition prediction model. It initiates with a fine partition and incrementally transitions towards a coarser subpartition of the true one, consistently maintaining piecewise equivariance in relation to the current partition.
As a result, the equivariance approximation error can be bounded solely in terms of (i) uncertainty quantification of the partition prediction, and (ii) bounds on the probability of failing to suggest a proper subpartition of the ground truth one.
We demonstrate the practical effectiveness of APEN using two data types exemplifying part-based symmetry: (i) real-world scans of room scenes containing multiple furniture-type objects; and, (ii) human motions, characterized by articulated parts exhibiting rigid movement. Our empirical results demonstrate the advantage of integrating piecewise $E(3)$ symmetry into network design, showing a distinct improvement in generalization accuracy compared to prior works for both classification and segmentation tasks | https://openreview.net/pdf/217fe0b33e83f17cf8576ad0efa83c94bd2b3b26.pdf |
DAM: Towards a Foundation Model for Forecasting | https://openreview.net/forum?id=4NhMhElWqP | https://openreview.net/forum?id=4NhMhElWqP | Luke Nicholas Darlow,Qiwen Deng,Ahmed Hassan,Martin Asenov,Rajkarn Singh,Artjom Joosen,Adam Barker,Amos Storkey | ICLR 2024,Poster | It is challenging to scale time series forecasting models such that they forecast accurately for multiple distinct domains and datasets, all with potentially different underlying collection procedures (e.g., sample resolution), patterns (e.g., periodicity), and prediction requirements (e.g., reconstruction vs. forecasting). We call this general task universal forecasting. Existing methods usually assume that input data is regularly sampled, and they forecast to pre-determined horizons, resulting in failure to generalise outside of the scope of their training. We propose the DAM -- a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time for forecasting to non-fixed horizons. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution, that enables an efficient global perspective of the underlying temporal dynamics while retaining focus on the recent history; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output, (3) the basis coefficients of a continuous function of time. We show that a single univariate DAM, trained on 25 time series datasets, either outperformed or closely matched existing SoTA models at multivariate long-term forecasting across 18 datasets, including 8 held-out for zero-shot transfer, even though these models were trained to specialise for each dataset-horizon combination. This single DAM excels at zero-shot transfer and very-long-term forecasting, performs well at imputation, is interpretable via basis function composition and attention, can be tuned for different inference-cost requirements, is robust to missing and irregularly sampled data by design. | https://openreview.net/pdf/e2340dd80d1126bd938cde5068b515d84112d7a8.pdf |
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity | https://openreview.net/forum?id=4N97bz1sP6 | https://openreview.net/forum?id=4N97bz1sP6 | Tanvir Mahmud,Saeed Amizadeh,Kazuhito Koishida,Diana Marculescu | ICLR 2024,Poster | Conditional sound separation in multi-source audio mixtures without having access to single source sound data during training is a long standing challenge. Existing mix-and-separate based methods suffer from significant performance drop with multi-source training mixtures due to the lack of supervision signal for single source separation cases during training. However, in the case of language-conditional audio separation, we do have access to corresponding text descriptions for each audio mixture in our training data, which can be seen as (rough) representations of the audio samples in the language modality. That raises the curious question of how to generate supervision signal for single-source audio extraction by leveraging the fact that single-source sounding language entities can be easily extracted from the text description. To this end, in this paper, we propose a generic bi-modal separation framework which can enhance the existing unsupervised frameworks to separate single-source signals in a target modality (i.e., audio) using the easily separable corresponding signals in the conditioning modality (i.e., language), without having access to single-source samples in the target modality during training. We empirically show that this is well within reach if we have access to a pretrained joint embedding model between the two modalities (i.e., CLAP). Furthermore, we propose to incorporate our framework into two fundamental scenarios to enhance separation performance. First, we show that our proposed methodology significantly improves the performance of purely unsupervised baselines by reducing the distribution shift between training and test samples. In particular, we show that our framework can achieve 71% boost in terms of Signal-to-Distortion Ratio (SDR) over the baseline, reaching 97.5% of the supervised learning performance. Second, we show that we can further improve the performance of the supervised learning itself by 17% if we augment it by our proposed weakly-supervised framework. Our framework achieves this by making large corpora of unsupervised data available to the supervised learning model as well as utilizing a natural, robust regularization mechanism through weak supervision from the language modality, and hence enabling a powerful semi-supervised framework for audio separation. Code is released at https://github.com/microsoft/BiModalAudioSeparation. | https://openreview.net/pdf/52d08c5e688a05b869c54fb8183f4390fd23042e.pdf |
Expected flow networks in stochastic environments and two-player zero-sum games | https://openreview.net/forum?id=uH0FGECSEI | https://openreview.net/forum?id=uH0FGECSEI | Marco Jiralerspong,Bilun Sun,Danilo Vucetic,Tianyu Zhang,Yoshua Bengio,Gauthier Gidel,Nikolay Malkin | ICLR 2024,Poster | Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), which extend GFlowNets to stochastic environments. We show that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design. We then extend the concept of EFlowNets to adversarial environments, proposing adversarial flow networks (AFlowNets) for two-player zero-sum games. We show that AFlowNets learn to find above 80% of optimal moves in Connect-4 via self-play and outperform AlphaZero in tournaments.
Code: https://github.com/GFNOrg/AdversarialFlowNetworks. | https://openreview.net/pdf/0771d5b9bd5c323b000881b4f991ef6d578b4c05.pdf |
Neural Polynomial Gabor Fields for Macro Motion Analysis | https://openreview.net/forum?id=dTlKCQuuxP | https://openreview.net/forum?id=dTlKCQuuxP | Chen Geng,Hong-Xing Yu,Sida Peng,Xiaowei Zhou,Jiajun Wu | ICLR 2024,Poster | We study macro motion analysis, where macro motion refers to the collection of all visually observable motions in a dynamic scene. Traditional filtering-based methods on motion analysis typically focus only on local and tiny motions, yet fail to represent large motions or 3D scenes. Recent dynamic neural representations can faithfully represent motions using correspondences, but they cannot be directly used for motion analysis. In this work, we propose Phase-based neural polynomial Gabor fields (Phase-PGF), which learns to represent scene dynamics with low-dimensional time-varying phases. We theoretically show that Phase-PGF has several properties suitable for macro motion analysis. In our experiments, we collect diverse 2D and 3D dynamic scenes and show that Phase-PGF enables dynamic scene analysis and editing tasks including motion loop detection, motion factorization, motion smoothing, and motion magnification. Project page: https://chen-geng.com/phasepgf | https://openreview.net/pdf/197d2b5bd3a5ed5b9ced292ea962a2991841d424.pdf |
Denoising Diffusion via Image-Based Rendering | https://openreview.net/forum?id=1JbsdayvhO | https://openreview.net/forum?id=1JbsdayvhO | Titas Anciukevičius,Fabian Manhardt,Federico Tombari,Paul Henderson | ICLR 2024,Poster | Generating 3D scenes is a challenging open problem, which requires synthesizing plausible content that is fully consistent in 3D space. While recent methods such as neural radiance fields excel at view synthesis and 3D reconstruction, they cannot synthesize plausible details in unobserved regions since they lack a generative capability. Conversely, existing generative methods are typically not capable of reconstructing detailed, large-scale scenes in the wild, as they use limited-capacity 3D scene representations, require aligned camera poses, or rely on additional regularizers. In this work, we introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. To achieve this, we make three contributions. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes, dynamically allocating more capacity as needed to capture details visible in each image. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images without the need for any additional supervision signal such as masks or depths. This supports 3D reconstruction and generation in a unified architecture. Third, we develop a principled approach to avoid trivial 3D solutions when integrating the image-based rendering with the diffusion model, by dropping out representations of some images. We evaluate the model on several challenging datasets of real and synthetic images, and demonstrate superior results on generation, novel view synthesis and 3D reconstruction. | https://openreview.net/pdf/cefa1b3e50f7db4d2660331b3bcddef98f91af65.pdf |
LEAP: Liberate Sparse-View 3D Modeling from Camera Poses | https://openreview.net/forum?id=KPmajBxEaF | https://openreview.net/forum?id=KPmajBxEaF | Hanwen Jiang,Zhenyu Jiang,Yue Zhao,Qixing Huang | ICLR 2024,Poster | Are camera poses necessary for multi-view 3D modeling? Existing approaches predominantly assume access to accurate camera poses. While this assumption might hold for dense views, accurately estimating camera poses for sparse views is often elusive. Our analysis reveals that noisy estimated poses lead to degraded performance for existing sparse-view 3D modeling methods. To address this issue, we present LEAP, a novel pose-free approach, therefore challenging the prevailing notion that camera poses are indispensable. LEAP discards pose-based operations and learns geometric knowledge from data. LEAP is equipped with a neural volume, which is shared across scenes and is parameterized to encode geometry and texture priors. For each incoming scene, we update the neural volume by aggregating 2D image features in a feature-similarity-driven manner. The updated neural volume is decoded into the radiance field, enabling novel view synthesis from any viewpoint. On both object-centric and bounded scene-level datasets, we show that LEAP significantly outperforms prior methods when they employ predicted poses from state-of-the-art pose estimators. Notably, LEAP performs on par with prior approaches that use ground-truth poses while running $400\times$ faster than PixelNeRF. We show LEAP generalizes to novel object categories and scenes, and learns knowledge closely resembles epipolar geometry. | https://openreview.net/pdf/81697ef92adbc76c9e565149d7605d52845d78ba.pdf |
Language Modeling Is Compression | https://openreview.net/forum?id=jznbgiynus | https://openreview.net/forum?id=jznbgiynus | Gregoire Deletang,Anian Ruoss,Paul-Ambroise Duquenne,Elliot Catt,Tim Genewein,Christopher Mattern,Jordi Grau-Moya,Li Kevin Wenliang,Matthew Aitchison,Laurent Orseau,Marcus Hutter,Joel Veness | ICLR 2024,Poster | It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model. | https://openreview.net/pdf/83396178411528f1d5578be2fb86d0e930d7ee96.pdf |
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views | https://openreview.net/forum?id=SgjAojPKb3 | https://openreview.net/forum?id=SgjAojPKb3 | Francis Engelmann,Fabian Manhardt,Michael Niemeyer,Keisuke Tateno,Federico Tombari | ICLR 2024,Poster | Large visual-language models (VLMs), like CLIP, enable open-set image segmentation to segment arbitrary concepts from an image in a zero-shot manner. This goes beyond the traditional closed-set assumption, i.e., where models can only segment classes from a pre-defined training set. More recently, first works on open-set segmentation in 3D scenes have appeared in the literature. These methods are heavily influenced by closed-set 3D convolutional approaches that process point clouds or polygon meshes. However, these 3D scene representations do not align well with the image-based nature of the visual-language models. Indeed, point cloud and 3D meshes typically have a lower resolution than images and the reconstructed 3D scene geometry might not project well to the underlying 2D image sequences used to compute pixel-aligned CLIP features. To address these challenges, we propose OpenNeRF which naturally operates on posed images and directly encodes the VLM features within the NeRF. This is similar in spirit to LERF, however our work shows that using pixel-wise VLM features (instead of global CLIP features) results in an overall less complex architecture without the need for additional DINO regularization. Our OpenNeRF further leverages NeRF’s ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images. For 3D point cloud segmentation on the Replica dataset, OpenNeRF outperforms recent open-vocabulary methods such as LERF and OpenScene by at least +4.9 mIoU. | https://openreview.net/pdf/8cfcd086bb55abcd08c8ecc773d988224fc45922.pdf |
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction | https://openreview.net/forum?id=8HCARN2hhw | https://openreview.net/forum?id=8HCARN2hhw | Guillaume Bono,Leonid Antsfeld,Assem Sadek,Gianluca Monaci,Christian Wolf | ICLR 2024,Poster | Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretation, whereas classical robotics addresses this with scene reconstruction resulting in some form of map, usually estimated with geometry and sensor models and/or learning. In this work we propose to learn an actionable representation of the scene independently of the targeted downstream task and without explicitly optimizing reconstruction. The learned representation is optimized by a blind auxiliary agent trained to navigate with it on multiple short sub episodes branching out from a waypoint and, most importantly, without any direct visual observation. We argue and show that the blindness property is important and forces the (trained) latent representation to be the only means for planning. With probing experiments we show that the learned representation optimizes navigability and not reconstruction. On downstream tasks we show that it is robust to changes in distribution, in particular the sim2real gap, which we evaluate with a real physical robot in a real office building, significantly improving performance. | https://openreview.net/pdf/2b5b56f45f498f5877f3de2ea54e150352e0a408.pdf |
Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency | https://openreview.net/forum?id=w4rODxXsmM | https://openreview.net/forum?id=w4rODxXsmM | Stone Tao,Arth Shukla,Tse-kai Chan,Hao Su | ICLR 2024,Poster | Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve complex tasks from sparse rewards. One direction includes augmenting RL with offline data demonstrating desired tasks, but past work often require a lot of high-quality demonstration data that is difficult to obtain, especially for domains such as robotics. Our approach consists of a reverse curriculum followed by a forward curriculum. Unique to our approach compared to past work is the ability to efficiently leverage more than one demonstration via a per-demonstration reverse curriculum generated via state resets. The result of our reverse curriculum is an initial policy that performs well on a narrow initial state distribution and helps overcome difficult exploration problems. A forward curriculum is then used to accelerate the training of the initial policy to perform well on the full initial state distribution of the task and improve demonstration and sample efficiency. We show how the combination of a reverse curriculum and forward curriculum in our method, RFCL, enables significant improvements in demonstration and sample efficiency compared against various state-of-the-art learning-from-demonstration baselines, even solving previously unsolvable tasks that require high precision and control. Website with code and visualizations are here: https://reverseforward-cl.github.io/ | https://openreview.net/pdf/a4e50c83e30246b73792b78ff16bbc0d7da5225a.pdf |
BatchPrompt: Accomplish more with less | https://openreview.net/forum?id=Agyicd577r | https://openreview.net/forum?id=Agyicd577r | Jianzhe Lin,Maurice Diesendruck,Liang Du,Robin Abraham | ICLR 2024,Poster | The ever-increasing token limits of large language models (LLMs) have enabled long context as input. Many LLMs are trained and fine-tuned to perform zero/few-shot inference using instruction-based prompts. Prompts typically include a detailed task instruction, several examples, and a single data point for inference. This baseline is referred to as “SinglePrompt” in this paper. In terms of token count, when the data input is small compared to instructions and examples, this results in lower token utilization, compared with encoder-based models like fine-tuned BERT. This cost inefficiency, affecting inference speed and compute budget, counteracts many of the benefits that LLMs offer. This paper aims to alleviate this problem by batching multiple data points in each prompt, a strategy we refer to as “BatchPrompt”. We improve token utilization by increasing the “density” of data points, however, this cannot be done naively. Simple batching can degrade performance, especially as batch size increases, and data points can yield different answers depending on their position within a prompt. To address the quality issue while retaining high token utilization, we introduce Batch Permutation and Ensembling (BPE) for BatchPrompt – a simple majority vote over repeated permutations of data, that recovers label quality at the cost of more token usage. To counterbalance this cost, we further propose Self-reflection-guided EArly Stopping (SEAS), which can terminate the voting process early for data points that the LLM handles confidently. Our comprehensive experimental evaluation demonstrates that BPE + SEAS can boost the performance of BatchPrompt by a striking margin on a range of popular NLP tasks, including question answering (Boolq), textual entailment (RTE), and duplicate questions identification (QQP). This performance is even competitive with/higher than single-data prompting (SinglePrompt), while using far fewer LLM calls and input tokens. At batch size 32, our BatchPrompt + BPE + SEAS uses 15.7% the number of LLM calls, and achieves: Boolq accuracy 90.6% → 90.9% with 27.4% tokens, QQP accuracy 87.2% → 88.4% with 18.6% tokens, RTE accuracy 91.5% → 91.1% with 30.8% tokens. We hope our simple yet effective approach will shed light on the future research of large language models. Code: github.com/microsoft/BatchPrompt | https://openreview.net/pdf/880b2ee1e1f9501f4fce0e65aec89f6f9628c0aa.pdf |
Large Language Models as Optimizers | https://openreview.net/forum?id=Bb4VGOWELI | https://openreview.net/forum?id=Bb4VGOWELI | Chengrun Yang,Xuezhi Wang,Yifeng Lu,Hanxiao Liu,Quoc V Le,Denny Zhou,Xinyun Chen | ICLR 2024,Poster | Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to our main application in prompt optimization, where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks. Code at https://github.com/google-deepmind/opro. | https://openreview.net/pdf/2d8735c68596ba1a54aeab8d239c75222d919657.pdf |
ContextRef: Evaluating Referenceless Metrics for Image Description Generation | https://openreview.net/forum?id=j0ZvKSNZiP | https://openreview.net/forum?id=j0ZvKSNZiP | Elisa Kreiss,Eric Zelikman,Christopher Potts,Nick Haber | ICLR 2024,Poster | Referenceless metrics (e.g., CLIPScore) use pretrained vision--language models to assess image descriptions directly without costly ground-truth reference texts. Such methods can facilitate rapid progress, but only if they truly align with human preference judgments. In this paper, we introduce ContextRef, a benchmark for assessing referenceless metrics for such alignment. ContextRef has two components: human ratings along a variety of established quality dimensions, and ten diverse robustness checks designed to uncover fundamental weaknesses. A crucial aspect of ContextRef is that images and descriptions are presented in context, reflecting prior work showing that context is important for description quality. Using ContextRef, we assess a variety of pretrained models, scoring functions, and techniques for incorporating context. None of the methods is successful with ContextRef, but we show that careful fine-tuning yields substantial improvements. ContextRef remains a challenging benchmark though, in large part due to the challenge of context dependence. | https://openreview.net/pdf/9db83db2ce08c604d1a213c98b1f917994b55a17.pdf |
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks | https://openreview.net/forum?id=h7nOCxFsPg | https://openreview.net/forum?id=h7nOCxFsPg | Federico Errica,Mathias Niepert | ICLR 2024,Poster | We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain the efficiency and efficacy of deep graph networks with the additional advantages of a probabilistic model. We show the model's competitiveness on scarce supervision scenarios, under missing data, and for graph classification in comparison to popular neural models. We complement the experiments with qualitative analyses on hyper-parameters and the model's ability to answer probabilistic queries. | https://openreview.net/pdf/7d36ab73afa78688e46aee4961b1f466116cc98e.pdf |
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion | https://openreview.net/forum?id=duyA42HlCK | https://openreview.net/forum?id=duyA42HlCK | Xian Liu,Jian Ren,Aliaksandr Siarohin,Ivan Skorokhodov,Yanyu Li,Dahua Lin,Xihui Liu,Ziwei Liu,Sergey Tulyakov | ICLR 2024,Poster | Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL·E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a large-scale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios. | https://openreview.net/pdf/0bc26f8e63c8e6a2bd734c404b5ba962dff33f98.pdf |
ZeroFlow: Scalable Scene Flow via Distillation | https://openreview.net/forum?id=FRCHDhbxZF | https://openreview.net/forum?id=FRCHDhbxZF | Kyle Vedder,Neehar Peri,Nathaniel Eliot Chodosh,Ishan Khatri,ERIC EATON,Dinesh Jayaraman,Yang Liu,Deva Ramanan,James Hays | ICLR 2024,Poster | Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process full-size point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feedforward methods are considerably faster, running on the order of tens to hundreds of milliseconds for full-size point clouds, but require expensive human supervision. To address both limitations, we propose _Scene Flow via Distillation_, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feedforward model. Our instantiation of this framework, _ZeroFlow_, achieves **state-of-the-art** performance on the _Argoverse 2 Self-Supervised Scene Flow Challenge_ while using zero human labels by simply training on large-scale, diverse unlabeled data. At test-time, ZeroFlow is over 1000$\times$ faster than label-free state-of-the-art optimization-based methods on full-size point clouds (34 FPS vs 0.028 FPS) and over 1000$\times$ cheaper to train on unlabeled data compared to the cost of human annotation (\\$394 vs ~\\$750,000). To facilitate further research, we will release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets. | https://openreview.net/pdf/fcb34199f731db7fefa1f87e8c73d5b131c23f9d.pdf |
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation | https://openreview.net/forum?id=8Q4uVOJ5bX | https://openreview.net/forum?id=8Q4uVOJ5bX | Jiayu Xiao,Henglei Lv,Liang Li,Shuhui Wang,Qingming Huang | ICLR 2024,Poster | Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey appropriate spatial composition specified by a layout instruction. In this work, we probe into zero-shot grounded T2I generation with diffusion models, that is, generating images corresponding to the input layout information without training auxiliary modules or finetuning diffusion models. We propose a **R**egion and **B**oundary (R&B) aware cross-attention guidance approach that gradually modulates the attention maps of diffusion model during generative process, and assists the model to synthesize images (1) with high fidelity, (2) highly compatible with textual input, and (3) interpreting layout instructions accurately. Specifically, we leverage the discrete sampling to bridge the gap between consecutive attention maps and discrete layout constraints, and design a region-aware loss to refine the generative layout during diffusion process. We further propose a boundary-aware loss to strengthen object discriminability within the corresponding regions. Experimental results show that our method outperforms existing state-of-the-art zero-shot grounded T2I generation methods by a large margin both qualitatively and quantitatively on several benchmarks.
Project page: https://sagileo.github.io/Region-and-Boundary. | https://openreview.net/pdf/7fd02b5f42df119fd8594d92234df2cc81a9afd5.pdf |
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations | https://openreview.net/forum?id=I1quoTXZzc | https://openreview.net/forum?id=I1quoTXZzc | Xinyue Xu,Yi Qin,Lu Mi,Hao Wang,Xiaomeng Li | ICLR 2024,Poster | Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts. However, (1) they often fail to capture the high-order, nonlinear interaction between concepts, e.g., correcting a predicted concept (e.g., “yellow breast”) does not help correct highly correlated concepts (e.g., “yellow belly”), leading to suboptimal final accuracy; (2) they cannot naturally quantify the complex conditional dependencies between different concepts and class labels (e.g., for an image with the class label “Kentucky Warbler” and a concept “black bill”, what is the probability that the model correctly predicts another concept “black crown”), therefore failing to provide deeper insight into how a black-box model works. In response to these limitations, we propose Energy-based Concept Bottleneck Models (ECBMs). Our ECBMs use a set of neural networks to define the joint energy of candidate (input, concept, class) tuples. With such a unified interface, prediction, concept correction, and conditional dependency quantification are then represented as conditional probabilities, which are generated by composing different energy functions. Our ECBMs address both limitations of existing CBMs, providing higher accuracy and richer concept interpretations. Empirical results show that our approach outperforms the state-of-the-art on real-world datasets. | https://openreview.net/pdf/a989226ecd6840452afd3f0afc474109aade94b5.pdf |
SpeechTokenizer: Unified Speech Tokenizer for Speech Language Models | https://openreview.net/forum?id=AF9Q8Vip84 | https://openreview.net/forum?id=AF9Q8Vip84 | Xin Zhang,Dong Zhang,Shimin Li,Yaqian Zhou,Xipeng Qiu | ICLR 2024,Poster | Current speech large language models build upon discrete speech representations,
which can be categorized into semantic tokens and acoustic tokens. However,
existing speech tokens are not specifically designed for speech language modeling. To assess the suitability of speech tokens for building speech language
models, we established the first benchmark, SLMTokBench. Our results indicate
that neither semantic nor acoustic tokens are ideal for this purpose. Therefore, we
propose SpeechTokenizer, a unified speech tokenizer for speech large language
models. SpeechTokenizer adopts the Encoder-Decoder architecture with residual
vector quantization (RVQ). Unifying semantic and acoustic tokens, SpeechTokenizer disentangles different aspects of speech information hierarchically across
different RVQ layers. Furthermore, We construct a Unified Speech Language
Model (USLM) leveraging SpeechTokenizer. Experiments show that SpeechTokenizer performs comparably to EnCodec in speech reconstruction and demonstrates
strong performance on the SLMTokBench benchmark. Also, USLM outperforms
VALL-E in zero-shot Text-to-Speech tasks. Code and models are available at
https://github.com/ZhangXInFD/SpeechTokenizer/. | https://openreview.net/pdf/3cb075c0117af3ccbf6e7fc3e510c84700c1d0c0.pdf |
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping | https://openreview.net/forum?id=ukidfml68f | https://openreview.net/forum?id=ukidfml68f | Zijie Pan,Jiachen Lu,Xiatian Zhu,Li Zhang | ICLR 2024,Poster | High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed to overcome this constraint by harnessing image generative models, pretrained on extensive curated web datasets, using knowledge transfer techniques like Score Distillation Sampling (SDS).
Efficiently addressing the requirements of high-resolution rendering often necessitates the adoption of latent representation-based models, such as the Latent Diffusion Model (LDM). In this framework, a significant challenge arises:
To compute gradients for individual image pixels, it is necessary to backpropagate gradients from the designated latent space through the frozen components of the image model, such as the VAE encoder used within LDM. However, this gradient propagation pathway has never been optimized, remaining uncontrolled during training.
We find that the unregulated gradients adversely affect the 3D model's capacity in acquiring texture-related information from the image generative model,
leading to poor quality appearance synthesis.
To address this overarching challenge, we propose an innovative operation termed Pixel-wise Gradient Clipping (PGC) designed for seamless integration into existing 3D generative models, thereby enhancing their synthesis quality. Specifically,
we control the magnitude of stochastic gradients by clipping the pixel-wise gradients efficiently,
while preserving crucial texture-related gradient directions.
Despite this simplicity and minimal extra cost, extensive experiments demonstrate the efficacy of our PGC
in enhancing the performance of existing 3D generative models
for high-resolution object rendering. | https://openreview.net/pdf/c0909bcd147c795dc8841fbfd0cd850bdd80e2d3.pdf |
Transferring Labels to Solve Annotation Mismatches Across Object Detection Datasets | https://openreview.net/forum?id=ChHx5ORqF0 | https://openreview.net/forum?id=ChHx5ORqF0 | Yuan-Hong Liao,David Acuna,Rafid Mahmood,James Lucas,Viraj Uday Prabhu,Sanja Fidler | ICLR 2024,Poster | In object detection, varying annotation protocols across datasets can result in annotation mismatches, leading to inconsistent class labels and bounding regions. Addressing these mismatches typically involves manually identifying common trends and fixing the corresponding bounding boxes and class labels. To alleviate this laborious process, we introduce the label transfer problem in object detection. Here, the goal is to transfer bounding boxes from one or more source datasets to match the annotation style of a target dataset. We propose a data-centric approach, Label-Guided Pseudo-Labeling (LGPL), that improves downstream detectors in a manner agnostic to the detector learning algorithms and model architectures. Validating across four object detection scenarios, defined over seven different datasets and three different architectures, we show that transferring labels for a target task via LGPL consistently improves the downstream detection in every setting, on average by $1.88$ mAP and 2.65 AP$^{75}$. Most importantly, we find that when training with multiple labeled datasets, carefully addressing annotation mismatches with LGPL alone can improve downstream object detection better than off-the-shelf supervised domain adaptation techniques that align instance features. | https://openreview.net/pdf/45eb6cf5c27fb10c1f8e659d74e5530fb2064767.pdf |
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency | https://openreview.net/forum?id=5EniAcsO7f | https://openreview.net/forum?id=5EniAcsO7f | Yannis Kalantidis,Mert Bülent Sarıyıldız,Rafael S. Rezende,Philippe Weinzaepfel,Diane Larlus,Gabriela Csurka | ICLR 2024,Poster | State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions, due to e.g. weather or time of day, with dramatic consequences on the visual localization accuracy. In this paper, we improve this retrieval step and tailor it to the final localization task. Among the several changes we advocate for, we propose to synthesize variants of the training set images, obtained from generative text-to-image models, in order to automatically expand the training set towards a number of nameable variations that particularly hurt visual localization. After expanding the training set, we propose a training approach that leverages the specificities and the underlying geometry of this mix of real and synthetic images. We experimentally show that those changes translate into large improvements for the most challenging visual localization datasets. | https://openreview.net/pdf/ce9b20038af890c0d8df5a681c61f3851e4466aa.pdf |
DreamClean: Restoring Clean Image Using Deep Diffusion Prior | https://openreview.net/forum?id=6ALuy19mPa | https://openreview.net/forum?id=6ALuy19mPa | Jie Xiao,Ruili Feng,Han Zhang,Zhiheng Liu,Zhantao Yang,Yurui Zhu,Xueyang Fu,Kai Zhu,Yu Liu,Zheng-Jun Zha | ICLR 2024,Poster | Image restoration poses a garners substantial interest due to the exponential surge in demands for recovering high-quality images from diverse mobile camera devices, adverse lighting conditions, suboptimal shooting environments, and frequent image compression for efficient transmission purposes. Yet this problem gathers significant challenges as people are blind to the type of restoration the images suffer, which, is usually the case in real-day scenarios and is most urgent to solve for this field. Current research, however, heavily relies on prior knowledge of the restoration type, either explicitly through rules or implicitly through the availability of degraded-clean image pairs to define the restoration process, and consumes considerable effort to collect image pairs of vast degradation types. This paper introduces DreamClean, a training-free method that needs no degradation prior knowledge but yields high-fidelity and generality towards various types of image degradation. DreamClean embeds the degraded image back to the latent of pre-trained diffusion models and re-sample it through a carefully designed diffusion process that mimics those generating clean images. Thanks to the rich image prior in diffusion models and our novel Variance Preservation Sampling (VPS) technique, DreamClean manages to handle various different degradation types at one time and reaches far more satisfied final quality than previous competitors. DreamClean relies on elegant theoretical supports to assure its convergence to clean image when VPS has appropriate parameters, and also enjoys superior experimental performance over various challenging tasks that could be overwhelming for previous methods when degradation prior is unavailable. | https://openreview.net/pdf/8df264fda7e505d1e62a114fd8578bb744c58b7b.pdf |
CausalLM is not optimal for in-context learning | https://openreview.net/forum?id=guRNebwZBb | https://openreview.net/forum?id=guRNebwZBb | Nan Ding,Tomer Levinboim,Jialin Wu,Sebastian Goodman,Radu Soricut | ICLR 2024,Poster | Recent empirical evidence indicates that transformer based in-context learning performs better when using a prefix language model (prefixLM), in which in-context samples can all attend to each other, compared to causal language models (causalLM), which use auto-regressive attention that prohibits in-context samples to attend to future samples. While this result is intuitive, it is not understood from a theoretical perspective. In this paper we take a theoretical approach and analyze the convergence behavior of prefixLM and causalLM under a certain parameter construction. Our analysis shows that both LM types converge to their stationary points at a linear rate, but that while prefixLM converges to the optimal solution of linear regression, causalLM convergence dynamics follows that of an online gradient descent algorithm, which is not guaranteed to be optimal even as the number of samples grows infinitely. We supplement our theoretical claims with empirical experiments over synthetic and real tasks and using various types of transformers. Our experiments verify that causalLM consistently underperforms prefixLM in all settings. | https://openreview.net/pdf/2532c5a044ed2dc8f0ebf02dcac4ffabcb17d685.pdf |
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon | https://openreview.net/forum?id=cphhnHjCvC | https://openreview.net/forum?id=cphhnHjCvC | Guillaume Bono,Leonid Antsfeld,Boris Chidlovskii,Philippe Weinzaepfel,Christian Wolf | ICLR 2024,Poster | Most recent work in goal oriented visual navigation resorts to large-scale machine learning in simulated environments. The main challenge lies in learning compact representations generalizable to unseen environments and in learning high-capacity perception modules capable of reasoning on high-dimensional input. The latter is particularly difficult when the goal is not given as a category ("ObjectNav") but as an exemplar image ("ImageNav"), as the perception module needs to learn a comparison strategy requiring to solve an underlying visual correspondence problem. This has been shown to be difficult from reward alone or with standard auxiliary tasks. We address this problem through a sequence of two pretext tasks, which serve as a prior for what we argue is one of the main bottleneck in perception, extremely wide-baseline relative pose estimation and visibility prediction in complex scenes. The first pretext task, cross-view completion is a proxy for the underlying visual correspondence problem, while the second task addresses goal detection and finding directly. We propose a new dual encoder with a large-capacity binocular ViT model and show that correspondence solutions naturally emerge from the training signals. Experiments show significant improvements and SOTA performance on the two benchmarks, ImageNav and the Instance-ImageNav variant, where camera intrinsics and height differ between observation and goal. | https://openreview.net/pdf/34278c5e203491d903e4dc9abbcc9f691231f461.pdf |
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects | https://openreview.net/forum?id=hywpSoHwgX | https://openreview.net/forum?id=hywpSoHwgX | Chunming He,Kai Li,Yachao Zhang,Yulun Zhang,Chenyu You,Zhenhua Guo,Xiu Li,Martin Danelljan,Fisher Yu | ICLR 2024,Poster | Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance. | https://openreview.net/pdf/6cfeff63c66d382e37eaa3b693f906e673138bd2.pdf |
Consistency-guided Prompt Learning for Vision-Language Models | https://openreview.net/forum?id=wsRXwlwx4w | https://openreview.net/forum?id=wsRXwlwx4w | Shuvendu Roy,Ali Etemad | ICLR 2024,Poster | We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models. Our approach improves the generalization of large foundation models when fine-tuned on downstream tasks in a few-shot setting. The basic idea of CoPrompt is to enforce a consistency constraint in the prediction of the trainable and pre-trained models to prevent overfitting on the downstream task. Additionally, we introduce the following two components into our consistency constraint to further boost the performance: enforcing consistency on two perturbed inputs and combining two dominant paradigms of tuning, prompting and adapter. Enforcing consistency on perturbed input serves to further regularize the consistency constraint, thereby improving generalization. Moreover, the integration of adapters and prompts not only enhances performance on downstream tasks but also offers increased tuning flexibility in both input and output spaces. This facilitates more effective adaptation to downstream tasks in a few-shot learning setting. Experiments show that CoPrompt outperforms existing methods on a range of evaluation suites, including base-to-novel generalization, domain generalization, and cross-dataset evaluation. On generalization, CoPrompt improves the state-of-the-art on zero-shot tasks and the overall harmonic mean over 11 datasets. Detailed ablation studies show the effectiveness of each of the components in CoPrompt. We make our code available at https://github.com/ShuvenduRoy/CoPrompt. | https://openreview.net/pdf/1c0748de5a13b85a9cc229e3aa529d7826bf4c01.pdf |
Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting | https://openreview.net/forum?id=WhgB5sispV | https://openreview.net/forum?id=WhgB5sispV | Zeyu Yang,Hongye Yang,Zijie Pan,Li Zhang | ICLR 2024,Poster | Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time is challenging due to scene complexity and temporal dynamics. Despite advancements in neural implicit models, limitations persist: (i) Inadequate Scene Structure: Existing methods struggle to reveal the spatial and temporal structure of dynamic scenes from directly learning the complex 6D plenoptic function. (ii) Scaling Deformation Modeling: Explicitly modeling scene element deformation becomes impractical for complex dynamics. To address these issues, we consider the spacetime as an entirety and propose to approximate the underlying spatio-temporal 4D volume of a dynamic scene by optimizing a collection of 4D primitives, with explicit geometry and appearance modeling. Learning to optimize the 4D primitives enables us to synthesize novel views at any desired time with our tailored rendering routine. Our model is conceptually simple, consisting of a 4D Gaussian parameterized by anisotropic ellipses that can rotate arbitrarily in space and time, as well as view-dependent and time-evolved appearance represented by the coefficient of 4D spherindrical harmonics. This approach offers simplicity, flexibility for variable-length video and end-to-end training, and efficient real-time rendering, making it suitable for capturing complex dynamic scene motions. Experiments across various benchmarks, including monocular and multi-view scenarios, demonstrate our 4DGS model's superior visual quality and efficiency. | https://openreview.net/pdf/09726070ed68d0da50112d11e1f45bef1d4f010f.pdf |
Language-Informed Visual Concept Learning | https://openreview.net/forum?id=juuyW8B8ig | https://openreview.net/forum?id=juuyW8B8ig | Sharon Lee,Yunzhi Zhang,Shangzhe Wu,Jiajun Wu | ICLR 2024,Poster | Our understanding of the visual world is centered around various concept axes, characterizing different aspects of visual entities. While different concept axes can be easily specified by language, e.g., color, the exact visual nuances along each axis often exceed the limitations of linguistic articulations, e.g., a particular style of painting. In this work, our goal is to learn a language-informed visual concept representation, by simply distilling large pre-trained vision-language models. Specifically, we train a set of concept encoders to encode the information pertinent to a set of language-informed concept axes, with an objective of reproducing the input image through a pre-trained Text-to-Image (T2I) model. To encourage better disentanglement of different concept encoders, we anchor the concept embeddings to a set of text embeddings obtained from a pre-trained Visual Question Answering (VQA) model. At inference time, the model extracts concept embeddings along various axes from new test images, which can be remixed to generate images with novel compositions of visual concepts. With a lightweight test-time finetuning procedure, it can also generalize to novel concepts unseen at training.
Project page at https://cs.stanford.edu/~yzzhang/projects/concept-axes. | https://openreview.net/pdf/a80359a7936a6734a82c60625d0d40e8cc5febf9.pdf |
Online Continual Learning for Interactive Instruction Following Agents | https://openreview.net/forum?id=7M0EzjugaN | https://openreview.net/forum?id=7M0EzjugaN | Byeonghwi Kim,Minhyuk Seo,Jonghyun Choi | ICLR 2024,Poster | In learning an embodied agent executing daily tasks via language directives, the literature largely assumes that the agent learns all training data at the beginning. We argue that such a learning scenario is less realistic, since a robotic agent is supposed to learn the world continuously as it explores and perceives it. To take a step towards a more realistic embodied agent learning scenario, we propose two continual learning setups for embodied agents; learning new behaviors (Behavior Incremental Learning, Behavior-IL) and new environments (Environment Incremental Learning, Environment-IL) For the tasks, previous ‘data prior’ based continual learning methods maintain logits for the past tasks. However, the stored information is often insufficiently learned information and requires task boundary information, which might not always be available. Here, we propose to update them based on confidence scores without task boundary information (i.e., task-free) in a moving average fashion, named Confidence-Aware Moving Average (CAMA). In the proposed challenging Behavior-IL and Environment-IL setups, our simple CAMA outperforms prior arts in our empirical validations by noticeable margins. | https://openreview.net/pdf/9bd1847dd40bdb80227f6ddd6480e1eee45a4999.pdf |
Localizing and Editing Knowledge In Text-to-Image Generative Models | https://openreview.net/forum?id=Qmw9ne6SOQ | https://openreview.net/forum?id=Qmw9ne6SOQ | Samyadeep Basu,Nanxuan Zhao,Vlad I Morariu,Soheil Feizi,Varun Manjunatha | ICLR 2024,Poster | Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have achieved unprecedented quality of photorealism with state-of-the-art FID scores on MS-COCO and other generation benchmarks. Given a caption, image generation requires fine-grained knowledge about attributes such as object structure, style, and viewpoint amongst others. Where does this information reside in text-to-image generative models? In our paper, we tackle this question and understand how knowledge corresponding to distinct visual attributes is stored in large-scale text-to-image diffusion models. We adapt Causal Mediation Analysis for text-to-image models and trace knowledge about distinct visual attributes to various (causal) components in the (i) UNet and (ii) text-encoder of the diffusion model.
In particular, we show that unlike large-language models, knowledge about different attributes is not localized in isolated components, but is instead distributed amongst a set of components in the conditional UNet. These sets of components are often distinct for different visual attributes (e.g., style} / objects). Remarkably, we find that the text-encoder in public text-to-image models such as Stable-Diffusion contains {\it only} one causal state across different visual attributes, and this is the first self-attention layer corresponding to the last subject token of the attribute in the caption. This is in stark contrast to the causal states in other language models which are often the mid-MLP layers. Based on this observation of only one causal state in the text-encoder, we introduce a fast, data-free model editing method DiffQuickFix which can effectively edit concepts (remove or update knowledge) in text-to-image models. DiffQuickFix can edit (ablate) concepts in under a second with a closed-form update, providing a significant 1000x speedup and comparable editing performance to existing fine-tuning based editing methods. | https://openreview.net/pdf/bb5747a05936ce67727ff4ff984fe4a8dee34966.pdf |
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization | https://openreview.net/forum?id=7NzgkEdGyr | https://openreview.net/forum?id=7NzgkEdGyr | Weiyang Liu,Zeju Qiu,Yao Feng,Yuliang Xiu,Yuxuan Xue,Longhui Yu,Haiwen Feng,Zhen Liu,Juyeon Heo,Songyou Peng,Yandong Wen,Michael J. Black,Adrian Weller,Bernhard Schölkopf | ICLR 2024,Poster | Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in computer vision and natural language. The results validate the effectiveness of BOFT as a generic finetuning method. | https://openreview.net/pdf/717cca7ed2cd7fd840370c3e76a702396744c7ea.pdf |
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization | https://openreview.net/forum?id=qtE9K23ISq | https://openreview.net/forum?id=qtE9K23ISq | Marin Scalbert,Maria Vakalopoulou,Florent Couzinie-Devy | ICLR 2024,Poster | In Self-Supervised Learning (SSL), models are typically pretrained, fine-tuned, and evaluated on the same domains. However, they tend to perform poorly when evaluated on unseen domains, a challenge that Unsupervised Domain Generalization (UDG) seeks to address. Current UDG methods rely on domain labels, which are often challenging to collect, and domain-specific architectures that lack scalability when confronted with numerous domains, making the current methodology impractical and rigid. Inspired by contrastive-based UDG methods that mitigate spurious correlations by restricting comparisons to examples from the same domain, we hypothesize that eliminating style variability within a batch could provide a more convenient and flexible way to reduce spurious correlations without requiring domain labels. To verify this hypothesis, we introduce Batch Styles Standardization (BSS), a relatively simple yet powerful Fourier-based method to standardize the style of images in a batch specifically designed for integration with SSL methods to tackle UDG. Combining BSS with existing SSL methods offers serious advantages over prior UDG methods: (1) It eliminates the need for domain labels or domain-specific network components to enhance domain-invariance in SSL representations, and (2) offers flexibility as BSS can be seamlessly integrated with diverse contrastive-based but also non-contrastive-based SSL methods. Experiments on several UDG datasets demonstrate that it significantly improves downstream task performances on unseen domains, often outperforming or rivaling UDG methods. Finally, this work clarifies the underlying mechanisms contributing to BSS's effectiveness in improving domain-invariance in SSL representations and performances on unseen domains. Implementations of the extended SSL methods and BSS are provided at this [url](https://gitlab.com/vitadx/articles/towards-domain-invariant-ssl-through-bss). | https://openreview.net/pdf/a0ec20c03c4c675c0d6beca59b771ba183f768af.pdf |
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving | https://openreview.net/forum?id=LsURkIPYR5 | https://openreview.net/forum?id=LsURkIPYR5 | Tianyu Li,Peijin Jia,Bangjun Wang,Li Chen,KUN JIANG,Junchi Yan,Hongyang Li | ICLR 2024,Poster | A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerlines. While simply predicting both types of lane in one model is mutually excluded in learning objective, we advocate lane segment as a new representation that seamlessly incorporates both geometry and topology information. Thus, we introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. Another is an identical initialization strategy for reference points, which enhances the learning of positional priors for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks, i.e., map element detection (+4.8 mAP), centerline perception (+6.9 DET$_l$), and the newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains a real-time inference speed of 14.7 FPS. Code is accessible at https://github.com/OpenDriveLab/LaneSegNet. | https://openreview.net/pdf/c017406a4e6d34007982fc886c82d80eb20d9ac1.pdf |
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips | https://openreview.net/forum?id=1SIBN5Xyw7 | https://openreview.net/forum?id=1SIBN5Xyw7 | Man Yao,JiaKui Hu,Tianxiang Hu,Yifan Xu,Zhaokun Zhou,Yonghong Tian,Bo XU,Guoqi Li | ICLR 2024,Poster | Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By contrast, no neuromorphic chips are designed especially for Transformer-based SNNs, which have just emerged, and their performance is only on par with CNN-based SNNs, offering no distinct advantage. In this work, we propose a general Transformer-based SNN architecture, termed as ``Meta-SpikeFormer", whose goals are: (1) *Lower-power*, supports the spike-driven paradigm that there is only sparse addition in the network; (2) *Versatility*, handles various vision tasks; (3) *High-performance*, shows overwhelming performance advantages over CNN-based SNNs; (4) *Meta-architecture*, provides inspiration for future next-generation Transformer-based neuromorphic chip designs. Specifically, we extend the Spike-driven Transformer in \citet{yao2023spike} into a meta architecture, and explore the impact of structure, spike-driven self-attention, and skip connection on its performance. On ImageNet-1K, Meta-SpikeFormer achieves 80.0\% top-1 accuracy (55M), surpassing the current state-of-the-art (SOTA) SNN baselines (66M) by 3.7\%. This is the first direct training SNN backbone that can simultaneously supports classification, detection, and segmentation, obtaining SOTA results in SNNs. Finally, we discuss the inspiration of the meta SNN architecture for neuromorphic chip design. | https://openreview.net/pdf/e5434cfb003a8fac2ecd80964dcac7ee61c10435.pdf |
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