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Dynamic Aggregated Network for Gait recognition

Dynamic Aggregated Network for Gait Recognition

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

作者： Ma, Kang Fu, Ying Zheng, Dezhi Cao, Chunshui Hu, Xuecai Huang, Yongzhen Beijing Inst Technol Beijing Peoples R China WATRIX AI Beijing Peoples R China Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Gait recognition is beneficial for a variety of applications, including video surveillance, crime scene investigation, and social security, to mention a few. However, gait recognition often suffers from multiple exterior factors in real scenes, such as carrying conditions, wearing overcoats, and diverse viewing angles. Recently, various deep learning-based gait recognition methods have achieved promising results, but they tend to extract one of the salient features using fixed-weighted convolutional networks, do not well consider the relationship within gait features in key regions, and ignore the aggregation of complete motion patterns. In this paper, we propose a new perspective that actual gait features include global motion patterns in multiple key regions, and each global motion pattern is composed of a series of local motion patterns. To this end, we propose a Dynamic Aggregation Network (DANet) to learn more discriminative gait features. Specifically, we create a dynamic attention mechanism between the features of neighboring pixels that not only adaptively focuses on key regions but also generates more expressive local motion patterns. In addition, we develop a self-attention mechanism to select representative local motion patterns and further learn robust global motion patterns. Extensive experiments on three popular public gait datasets, i.e., CASIA-B, OUMVLP, and Gait3D, demonstrate that the proposed method can provide substantial improvements over the current state-of-the-art methods.

关键词： body gesture Humans: Face movement pose

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Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

Iterative Next Boundary Detection for Instance Segmentation ...

引用

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

作者： Gillert, Alexander Resente, Giulia Anadon-Rosell, Alba Wilmking, Martin von Lukas, Uwe Freiherr Fraunhofer Inst Comp Graph Res IGD Rostock Germany Ernst Moritz Arndt Univ Inst Bot & Landscape Ecol Greifswald Germany Ctr Res Ecol & Forestry Applicat CREAF Barcelona Spain Univ Rostock Inst Visual & Analyt Comp Rostock Germany

ISBN: (纸本)9798350301298

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://***/alexander-g/INBD.

关键词： cell microscopy Medical and biological vision

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DeCo : Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-to-Fine Contrastive Ranking

DeCo : Decomposition and Reconstruction for Compositional Te...

引用

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

作者： Yang, Lijin Kong, Quan Yang, Hsuan-Kung Kehl, Wadim Sato, Yoichi Kobori, Norimasa Univ Tokyo Tokyo Japan Woven Toyota Tokyo Japan

ISBN: (纸本)9798350301298

Understanding dense action in videos is a fundamental challenge towards the generalization of vision models. Several works show that compositionality is key to achieving generalization by combining known primitive elements, especially for handling novel composited structures. Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding. In recent works, composition is assumed to be learned from pairs of whole videos and language embeddings through large scale self-supervised pre-training. Alternatively, one can process the video and language into word-level primitive elements, and then only learn fine-grained semantic correspondences. Both approaches do not consider the granularity of the compositions, where different query granularity corresponds to different video segments. Therefore, a good compositional representation should be sensitive to different video and query granularity. We propose a method to learn a coarse-to-fine compositional representation by decomposing the original query sentence into different granular levels, and then learning the correct correspondences between the video and recombined queries through a contrastive ranking constraint. Additionally, we run temporal boundary prediction in a coarse-to-fine manner for precise grounding boundary detection. Experiments are performed on two datasets, Charades-CG and ActivityNet-CG, showing the superior compositional generalizability of our approach.

关键词： and reasoning language vision

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Are Deep Neural Networks SMARTer than Second Graders?

Are Deep Neural Networks SMARTer than Second Graders?

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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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BiFormer: vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

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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

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

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

作者： Shoman, Maged Wang, Dongdong Aboah, Armstrong Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL 32816 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA Univ Cent Florida Joint Appointment Dept Comp Sci Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL USA

ISBN: (纸本)9798350365474

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The opensource code will be available at https://***/UCF-SST-Lab/AICity2024cvprW

关键词： computer vision cross-modality learning dense video captioning domain adaptation natural language processing

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Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

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

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9798350365474

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset's specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： action recognition

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Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient vision Transformers

Sparsifiner: Learning Sparse Instance-Dependent Attention fo...

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

作者： Wei, Cong Duke, Brendan Jiang, Ruowei Aarabi, Parham Taylor, Graham W. Shkurti, Florian Univ Toronto Toronto ON Canada Univ Guelph Guelph ON N1G 2W1 Canada Modiface Inc Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350301298

vision Transformers (ViT) have shown competitive advantages in terms of performance compared to convolutional neural networks (CNNs), though they often come with high computational costs. To this end, previous methods explore different attention patterns by limiting a fixed number of spatially nearby tokens to accelerate the ViT's multi-head self-attention (MHSA) operations. However, such structured attention patterns limit the token-to-token connections to their spatial relevance, which disregards learned semantic connections from a full attention mask. In this work, we propose an approach to learn instance-dependent attention patterns, by devising a lightweight connectivity predictor module that estimates the connectivity score of each pair of tokens. Intuitively, two tokens have high connectivity scores if the features are considered relevant either spatially or semantically. As each token only attends to a small number of other tokens, the binarized connectivity masks are often very sparse by nature and therefore provide the opportunity to reduce network FLOPs via sparse computations. Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto frontier between FLOPs and top-1 accuracy on ImageNet compared to token sparsity. Our method reduces 48% similar to 69% FLOPs of MHSA while the accuracy drop is within 0.4%. We also show that combining attention and token sparsity reduces ViT FLOPs by over 60%.

关键词： Efficient and scalable vision

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DETRs with Hybrid Matching

DETRs with Hybrid Matching

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

作者： Jia, Ding Yuan, Yuhui He, Haodi Wu, Xiaopei Yu, Haojun Lin, Weihong Sun, Lei Zhang, Chao Hu, Han Peking Univ Beijing Peoples R China Stanford Univ Stanford CA USA Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. Code is available at: https://***/HDETR.

关键词： detection recognition: Categorization retrieval

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Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

Guiding Pseudo-labels with Uncertainty Estimation for Source...

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

作者： Litrico, Mattia Del Bue, Alessio Morerio, Pietro Ist Italiano Tecnol Pattern Anal & Comp Vision PAVIS Genoa Italy

ISBN: (纸本)9798350301298

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

关键词： continual low-shot meta or long-tail learning Transfer

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