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共 21007 条记录，以下是1131-1140 订阅

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Slide-Transformer: Hierarchical vision Transformer with Local Self-Attention

Slide-Transformer: Hierarchical Vision Transformer with Loca...

引用

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

作者： Pan, Xuran Ye, Tianzhu Xia, Zhuofan Song, Shiji Huang, Gao Tsinghua Univ BNRist Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350301298

Self-attention mechanism has been a key factor in the recent progress of vision Transformer (ViT), which enables adaptive feature extraction from global contexts. However, existing self-attention methods either adopt sparse global attention or window attention to reduce the computation complexity, which may compromise the local feature learning or subject to some handcrafted designs. In contrast, local attention, which restricts the receptive field of each query to its own neighboring pixels, enjoys the benefits of both convolution and self-attention, namely local inductive bias and dynamic feature selection. Nevertheless, current local attention modules either use inefficient Im2Col function or rely on specific CUDA kernels that are hard to generalize to devices without CUDA support. In this paper, we propose a novel local attention module, Slide Attention, which leverages common convolution operations to achieve high efficiency, flexibility and generalizability. Specifically, we first re-interpret the column-based Im2Col function from a new row-based perspective and use Depthwise Convolution as an efficient substitution. On this basis, we propose a deformed shifting module based on the re-parameterization technique, which further relaxes the fixed key/value positions to deformed features in the local region. In this way, our module realizes the local attention paradigm in both efficient and flexible manner. Extensive experiments show that our slide attention module is applicable to a variety of advanced vision Transformer models and compatible with various hardware devices, and achieves consistently improved performances on comprehensive benchmarks.

关键词： Deep learning architectures and techniques

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GaitGCI: Generative Counterfactual Intervention for Gait recognition

GaitGCI: Generative Counterfactual Intervention for Gait Rec...

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

作者： Dou, Huanzhang Zhang, Pengyi Su, Wei Yu, Yunlong Lin, Yining Li, Xi Zhejiang Univ Coll Comp Sci Technol Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci Elect Engn Hangzhou Peoples R China SupreMind Shanghai Peoples R China Zhejiang Univ Shanghai Inst Adv Study Hangzhou Peoples R China Shanghai AI Lab Shanghai Peoples R China Zhejiang Singapore Innovat & Joint Res Lab Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern;2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost;3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild).

关键词： Biometrics

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Event-Based Frame Interpolation with Ad-hoc Deblurring

Event-Based Frame Interpolation with Ad-hoc Deblurring

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

作者： Sun, Lei Sakaridis, Christos Liang, Jingyun Sun, Peng Cao, Jiezhang Zhang, Kai Jiang, Qi Wang, Kaiwei Van Gool, Luc Zhejiang Univ Hangzhou Peoples R China Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of the motion, and assume either that the input video is sharp, restricting themselves to frame interpolation, or that it is blurry, including an explicit, separate deblurring stage before interpolation in their pipeline. We instead propose a general method for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that naturally incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. In addition, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments on the standard GoPro benchmark and on our dataset show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring and the joint task of interpolation and deblurring. Our code and dataset are available at https://***/AHupuJR/REFID.

关键词： Low-level vision

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Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face recognition

Sibling-Attack: Rethinking Transferable Adversarial Attacks ...

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

作者： Li, Zexin Yin, Bangjie Yao, Taiping Guo, Junfeng Ding, Shouhong Chen, Simin Liu, Cong Univ Calif Riverside Riverside CA USA Univ Texas Dallas Richardson TX USA Tencent Shenzhen Peoples R China

ISBN: (纸本)9798350301298

A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information to attackers. While recent research took an important step towards attacking black-box FR models through leveraging transferability, their performance is still limited, especially against online commercial FR systems that can be pessimistic (e.g., a less than 50% ASR-attack success rate on average). Motivated by this, we present Sibling-Attack, a new FR attack technique for the first time explores a novel multi-task perspective (i.e., leveraging extra information from multi-correlated tasks to boost attacking transferability). Intuitively, Sibling-Attack selects a set of tasks correlated with FR and picks the Attribute recognition (AR) task as the task used in Sibling-Attack based on theoretical and quantitative analysis. Sibling-Attack then develops an optimization framework that fuses adversarial gradient information through (1) constraining the cross-task features to be under the same space, (2) a joint-task meta optimization framework that enhances the gradient compatibility among tasks, and (3) a cross-task gradient stabilization method which mitigates the oscillation effect during attacking. Extensive experiments demonstrate that SiblingAttack outperforms state-of-the-art FR attack techniques by a non-trivial margin, boosting ASR by 12.61% and 55.77% on average on state-of-the-art pre-trained FR models and two well-known, widely used commercial FR systems.

关键词： Adversarial attack and defense

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Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

Learning Semantic-Aware Knowledge Guidance for Low-Light Ima...

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

作者： Wu, Yuhui Pan, Chen Wang, Guoqing Yang, Yang Wei, Jiwei Li, Chongyi Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement

关键词： Low-level vision

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A Soma Segmentation Benchmark in Full Adult Fly Brain

A Soma Segmentation Benchmark in Full Adult Fly Brain

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

作者： Liu, Xiaoyu Hu, Bo Li, Mingxing Huang, Wei Zhang, Yueyi Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Neuron reconstruction in a full adult fly brain from high-resolution electron microscopy (EM) data is regarded as a cornerstone for neuroscientists to explore how neurons inspire intelligence. As the central part of neurons, somas in the full brain indicate the origin of neurogenesis and neural functions. However, due to the absence of EM datasets specifically annotated for somas, existing deep learning-based neuron reconstruction methods cannot directly provide accurate soma distribution and morphology. Moreover, full brain neuron reconstruction remains extremely time-consuming due to the unprecedentedly large size of EM data. In this paper, we develop an efficient soma reconstruction method for obtaining accurate soma distribution and morphology information in a full adult fly brain. To this end, we first make a high-resolution EM dataset with fine-grained 3D manual annotations on somas. Relying on this dataset, we propose an efficient, two-stage deep learning algorithm for predicting accurate locations and boundaries of 3D soma instances. Further, we deploy a parallelized, high-throughput data processing pipeline for executing the above algorithm on the full brain. Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective. We release our code and dataset at https://***/liuxy1103/EMADS.

关键词： cell microscopy Medical and biological vision

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Active Exploration of Multimodal Complementarity for Few-Shot Action recognition

Active Exploration of Multimodal Complementarity for Few-Sho...

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

作者： Wanyan, Yuyang Yang, Xiaoshan Chen, Chaofan Xu, Changsheng Chinese Acad Sci CASIA State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing Peoples R China UCAS Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China USTC Sch Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Recently, few-shot action recognition receives increasing attention and achieves remarkable progress. However, previous methods mainly rely on limited unimodal data (e.g., RGB frames) while the multimodal information remains relatively underexplored. In this paper, we propose a novel Active Multimodal Few-shot Action recognition (AMFAR) framework, which can actively find the reliable modality for each sample based on task-dependent context information to improve few-shot reasoning procedure. In meta-training, we design an Active Sample Selection (ASS) module to organize query samples with large differences in the reliability of modalities into different groups based on modality-specific posterior distributions. In addition, we design an Active Mutual Distillation (AMD) to capture discriminative task-specific knowledge from the reliable modality to improve the representation learning of unreliable modality by bidirectional knowledge distillation. In meta-test, we adopt Adaptive Multimodal Inference (AMI) to adaptively fuse the modality-specific posterior distributions with a larger weight on the reliable modality. Extensive experimental results on four public benchmarks demonstrate that our model achieves significant improvements over existing unimodal and multimodal methods.

关键词： Video: Action and event understanding

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CoWs on PASTURE: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation

CoWs on PASTURE: Baselines and Benchmarks for Language-Drive...

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

作者： Gadre, Sarnir Yitzhak Wortsman, Mitchell Ilharco, Gabriel Schmidt, Ludwig Song, Shuran Columbia Univ New York NY 10027 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350301298

For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the PASTURE benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 22 CoW baselines across HABITAT, ROBOTHOR, and PASTURE. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration-and no additional training-matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on HABITAT MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art ROBOTHOR ZSON model.(1)

关键词： Embodied vision: Active agents simulation

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Improving vision-and-Language Navigation by Generating Future-View Image Semantics

Improving Vision-and-Language Navigation by Generating Futur...

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

作者： Li, Jialu Bansal, Mohit UNC Chapel Hill Chapel Hill NC 27599 USA

ISBN: (纸本)9798350301298

vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent can benefit from generating the potential future view during navigation. Intuitively, humans will have an expectation of how the future environment will look like, based on the natural language instructions and surrounding views, which will aid correct navigation. Hence, to equip the agent with this ability to generate the semantics of future navigation views, we first propose three proxy tasks during the agent's in-domain pre-training: Masked Panorama Modeling (MPM), Masked Trajectory Modeling (MTM), and Action Prediction with Image Generation (APIG). These three objectives teach the model to predict missing views in a panorama (MPM), predict missing steps in the full trajectory (MTM), and generate the next view based on the full instruction and navigation history (APIG), respectively. We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step. Empirically, our VLN-SIG achieves the new state-of-the-art on both Room-to-Room dataset and CVDN dataset. We further show that our agent learns to fill in missing patches in future views qualitatively, which brings more interpretability over agents' predicted actions. Lastly, we demonstrate that learning to predict future view semantics also enables the agent to have better performance on longer paths.

关键词： language reasoning vision

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DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration

DR2: Diffusion-based Robust Degradation Remover for Blind Fa...

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

作者： Wang, Zhixin Zhang, Ziying Zhang, Xiaoyun Zheng, Huangjie Zhou, Mingyuan Zhang, Ya Wang, Yanfeng Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Univ Texas Austin Austin TX 78712 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. To tackle this robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image. By leveraging a well-performing denoising diffusion probabilistic model, our DR2 diffuses input images to a noisy status where various types of degradation give way to Gaussian noise, and then captures semantic information through iterative denoising steps. As a result, DR2 is robust against common degradation (e.g. blur, resize, noise and compression) and compatible with different designs of enhancement modules. Experiments in various settings show that our framework outperforms state-of-the-art methods on heavily degraded synthetic and real-world datasets.

关键词： Low-level vision

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