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BiFormer: vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Lei Wang, Xinjiang Ke, Zhanghan Zhang, Wayne Lau, Rynson City Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pair-wise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bi-level routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://***/rayleizhu/BiFormer.

关键词： Deep learning architectures and techniques

来源：评论

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A Large-scale Robustness Analysis of Video Action recognition Models

A Large-scale Robustness Analysis of Video Action Recognitio...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Schiappa, Madeline Chantry Biyani, Naman Kamtam, Prudvi Vyas, Shruti Palangi, Hamid Vineet, Vibhav Rawat, Yogesh Univ Cent Florida CRCV Orlando FL 32816 USA IIT Kanpur Kanpur Uttar Pradesh India Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

We have seen a great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance on existing benchmarks. In this work, we perform a large-scale robustness analysis of these existing models for video action recognition. We focus on robustness against real-world distribution shift perturbations instead of adversarial perturbations. We propose four different benchmark datasets, HMDB51-P, UCF101-P, Kinetics400-P, and SSv2-P to perform this analysis. We study robustness of six state-of-the-art action recognition models against 90 different perturbations. The study reveals some interesting findings, 1) transformer based models are consistently more robust compared to CNN based models, 2) Pretraining improves robustness for Transformer based models more than CNN based models, and 3) All of the studied models are robust to temporal perturbations for all datasets but SSv2;suggesting the importance of temporal information for action recognition varies based on the dataset and activities. Next, we study the role of augmentations in model robustness and present a real-world dataset, UCF101-DS, which contains realistic distribution shifts, to further validate some of these findings. We believe this study will serve as a benchmark for future research in robust video action recognition

关键词： Datasets and evaluation

来源：评论

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RIFormer: Keep Your vision Backbone Effective But Removing Token Mixer

RIFormer: Keep Your Vision Backbone Effective But Removing T...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Jiahao Zhang, Songyang Liu, Yong Wu, Taiqiang Yang, Yujiu Liu, Xihui Chen, Kai Luo, Ping Lin, Dahua Shanghai AI Lab Shanghai Peoples R China Univ HongKong Hong Kong Peoples R China Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798350301298

This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design.

关键词： Deep learning architectures and techniques

来源：评论

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Are Deep Neural Networks SMARTer than Second Graders?

Are Deep Neural Networks SMARTer than Second Graders?

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cherian, Anoop Peng, Kuan-Chuan Lohit, Suhas Smith, Kevin A. Tenenbaum, Joshua B. Mitsubishi Elect Res Labs Cambridge MA 02139 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, question answering (e.g., ChatGPT), etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset1, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6-8 age group. Our dataset consists of 101 unique puzzles;each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle while retaining their solution algorithm. To benchmark the performance on the SMART-101 dataset, we propose a vision-and-language meta-learning model that can incorporate varied state-of-the-art neural backbones. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles in a supervised setting, they are not better than random accuracy when analyzed for generalization - filling this gap may demand new multimodal learning approaches.

关键词： language reasoning vision

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Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring

Deep Discriminative Spatial and Temporal Network for Efficie...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pan, Jinshan Xu, Boming Dong, Jiangxin Ge, Jianjun Tang, Jinhui Nanjing Univ Sci & Technol Nanjing Peoples R China China Elect Technol Grp Corp Beijing Peoples R China

ISBN: (纸本)9798350301298

How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity.

关键词： Low-level vision

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ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

ShadowDiffusion: When Degradation Prior Meets Diffusion Mode...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Guo, Lanqing Wang, Chong Yang, Wenhan Huang, Siyu Wang, Yufei Pfister, Hanspeter Wen, Bihan Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350301298

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset.(1)

关键词： Low-level vision

来源：评论

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Weak-shot Object Detection through Mutual Knowledge Transfer

Weak-shot Object Detection through Mutual Knowledge Transfer

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Du, Xuanyi Wan, Weitao Sun, Chong Li, Chen WeChat Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and target (T) datasets in a bi-directional manner. We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset. By jointly optimizing the classification loss and the proposed KT loss, the multiple instance learning module effectively learns to classify object proposals into novel categories in the T dataset with the transferred knowledge from base categories in the S dataset. Noticing the predicted boxes on the T dataset can be regarded as an extension for the original annotations on the S dataset to refine the proposal generator in return, we further propose a novel Consistency Filtering (CF) method to reliably remove inaccurate pseudo labels by evaluating the stability of the multiple instance learning module upon noise injections. Via mutually transferring knowledge between the S and T datasets in an iterative manner, the detection performance on the target dataset is significantly improved. Extensive experiments on public benchmarks validate that the proposed method performs favourably against the state-of-the-art methods without increasing the model parameters or inference computational complexity.

关键词： detection recognition: Categorization retrieval

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OpenMix: Exploring Outlier Samples for Misclassification Detection

OpenMix: Exploring Outlier Samples for Misclassification Det...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Fei Cheng, Zhen Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat MAIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9798350301298

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. The code is publicly available at https://***/Impression2805/OpenMix.

关键词： detection recognition: Categorization retrieval

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Reinforcement Learning-Based Black-Box Model Inversion Attacks

Reinforcement Learning-Based Black-Box Model Inversion Attac...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Han, Gyojin Choi, Jaehyun Lee, Haeil Kim, Junmo Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9798350301298

Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model. Recently, white-box model inversion attacks leveraging Generative Adversarial Networks (GANs) to distill knowledge from public datasets have been receiving great attention because of their excellent attack performance. On the other hand, current black-box model inversion attacks that utilize GANs suffer from issues such as being unable to guarantee the completion of the attack process within a predetermined number of query accesses or achieve the same level of performance as white-box attacks. To overcome these limitations, we propose a reinforcement learning-based black-box model inversion attack. We formulate the latent space search as a Markov Decision Process (MDP) problem and solve it with reinforcement learning. Our method utilizes the confidence scores of the generated images to provide rewards to an agent. Finally, the private data can be reconstructed using the latent vectors found by the agent trained in the MDP. The experiment results on various datasets and models demonstrate that our attack successfully recovers the private information of the target model by achieving state-of-the-art attack performance. We emphasize the importance of studies on privacy-preserving machine learning by proposing a more advanced black-box model inversion attack.

关键词： accountability ethics in vision fairness privacy Transparency

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Learning a Simple Low-light Image Enhancer from Paired Low-light Instances

Learning a Simple Low-light Image Enhancer from Paired Low-l...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fu, Zhenqi Yang, Yan Tu, Xiaotong Huang, Yue Ding, Xinghao Ma, Kai-Kuang Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9798350301298

Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in low-light conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https: //***/zhenqifu/PairLIE.

关键词： Low-level vision

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