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Learning Visual Prompt for Gait recognition

Learning Visual Prompt for Gait Recognition

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

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

ISBN: (纸本)9798350353013;9798350353006

Gait, a prevalent and complex form of human motion, plays a significant role in the field of long-range pedestrian retrieval due to the unique characteristics inherent in individual motion patterns. However, gait recognition in real-world scenarios is challenging due to the limitations of capturing comprehensive cross-viewing and cross-clothing data. Additionally, distractors such as occlusions, directional changes, and lingering movements further complicate the problem. The widespread application of deep learning techniques has led to the development of various potential gait recognition methods. However, these methods utilize convolutional networks to extract shared information across different views and attire conditions. Once trained, the parameters and non-linear function become constrained to fixed patterns, limiting their adaptability to various distractors in real-world scenarios. In this paper, we present a unified gait recognition framework to extract global motion patterns and develop a novel dynamic transformer to generate representative gait features. Specifically, we develop a trainable part-based prompt pool with numerous key-value pairs that can dynamically select prompt templates to incorporate into the gait sequence, thereby providing task-relevant shared knowledge information. Furthermore, we specifically design dynamic attention to extract robust motion patterns and address the length generalization issue. Extensive experiments on four widely recognized gait datasets, i.e., Gait3D, GREW, OUMVLP, and CASIA-B, reveal that the proposed method yields substantial improvements compared to current state-of-the-art approaches.

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AHIVE: Anatomy-aware Hierarchical vision Encoding for Interactive Radiology Report Retrieval

AHIVE: Anatomy-aware Hierarchical Vision Encoding for Intera...

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

作者： Yan, Sixing Cheung, William K. Tsang, Ivor W. Chiu, Keith Tong, Terence M. Cheung, Ka Chun Seel, Simon Hong Kong Baptist Univ Hong Kong Peoples R China Agcy Sci Technol & Res CFAR Singapore Singapore Agcy Sci Technol & Res IHPC Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Univ Technol Sydney AAII Sydney NSW Australia Queen Elizabeth Hosp Hong Kong Peoples R China Kwong Wah Hosp Hong Kong Peoples R China Tuen Mun Hosp Hong Kong Peoples R China NVIDIA Corp NVIDIA AI Technol Ctr Santa Clara CA USA

ISBN: (纸本)9798350353006

Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation, where irrelevant and unfaithful contents are unavoidable. The retrieval-based approach alleviates the limitation by identifying reports which are relevant to the input to assist the generation. To achieve clinically accurate report retrieval, we make reference to clinicians' diagnostic steps of examining a radiology image where anatomical and diagnostic details are typically focused, and propose a novel hierarchical visual concept representation called anatomy-aware hierarchical vision encoding (AHIVE). To learn AHIVE, we first derive a methodology to extract hierarchical diagnostic descriptions from radiology reports and develop a CLIP-based framework for the model training. Also, the hierarchical architecture of AHIVE is designed to support interactive report retrieval so that report revision made at one layer can be propagated to the subsequent ones to trigger other necessary revisions. We conduct extensive experiments and show that AHIVE can outperform the SOTA vision-language retrieval methods in terms of clinical accuracy by a large margin. We provide also a case study to illustrate how it enables interactive report retrieval.

关键词： image-to-text retrieval interactive retrieval system radiology imaging radiology report retrieval

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DUSt3R: Geometric 3D vision Made Easy

DUSt3R: Geometric 3D Vision Made Easy

引用

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

作者： Wang, Shuzhe Leroyt, Vincent Cabon, Yohann Chidlovskii, Boris Revaud, Jerome Aalto Univ Espoo Finland Naver Labs Europe Meylan France

ISBN: (纸本)9798350353006

Multi-view stereo reconstruction (MVS) in the wild re-quires to first estimate the camera intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain, yet they are mandatory to triangulate corresponding pixels in 3D space, which is at the core of all best performing MVS algorithms. In this work, we take an opposite stance and introduce DUSt3R, a radically novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections, operating without prior information about camera calibration nor viewpoint poses. We cast the pairwise reconstruction problem as a regression of pointmaps, relaxing the hard constraints of usual projective camera models. We show that this formulation smoothly unifies the monocular and binocular reconstruction cases. In the case where more than two images are provided, we further propose a simple yet effective global alignment strategy that expresses all pairwise pointmaps in a common reference frame. We base our network architecture on standard Transformer encoders and decoders, allowing us to leverage powerful pretrained models. Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, focal lengths, relative and absolute cameras. Extensive experiments on all these tasks showcase how DUSt3R effectively unifies various 3D vision tasks, setting new performance records on monocular & multi-view depth estimation as well as relative pose estimation. In summary, DUSt3R makes many geometric 3D vision tasks easy. Code and mod-els at https://***/naver/dust3r.

关键词： 3D reconstruction camera calibration foundation model monocular depth multi-view depth multi-view pose estimation visual localization

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Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Good at captioning, bad at counting: Benchmarking GPT-4V on ...

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

作者： Zhang, Chenhui Wang, Sherrie MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350365474

Large vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess world knowledge that leads to strong performance on location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting. Our benchmark is publicly available on this website. A full version of this paper can be found here.

关键词： benchmark Earth observation foundation model vision-language model

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Explaining CLIP's performance disparities on data from blind/low vision users

Explaining CLIP's performance disparities on data from blind...

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

作者： Massiceti, Daniela Longden, Camilla Slowik, Agnieszka Wills, Samuel Grayson, Martin Morrison, Cecily Microsoft Res Redmond WA 98052 USA World Bank 1818 H St NW Washington DC 20433 USA

ISBN: (纸本)9798350353006

Large multi-modal models (LMMs) hold the potential to usher in a new era of automated visual assistance for people who are blind or low vision (BLV). Yet, these models have not been systematically evaluated on data captured by BLV users. We address this by empirically assessing CLIP, a widely-used LMM likely to underpin many assistive technologies. Testing 25 CLIP variants in a zero-shot classification task, we find that their accuracy is 15 percentage points lower on average for images captured by BLV users than web-crawled images. This disparity stems from CLIP's sensitivities to 1) image content (e.g. not recognizing disability objects as well as other objects);2) image quality (e.g. not being robust to lighting variation);and 3) text content (e.g. not recognizing objects described by tactile adjectives as well as visual ones). We delve deeper with a textual analysis of three common pre-training datasets: LAION-400M, LAION-2B and DataComp-1B, showing that disability content is rarely mentioned. We then provide three examples that illustrate how the performance disparities extend to three downstream models underpinned by CLIP: OWL-ViT, CLIPSeg and DALL-E2. We find that few-shot learning with as few as 5 images can mitigate CLIP's quality-of-service disparities for BLV users in some scenarios, which we discuss alongside a set of other possible mitigations.

关键词： accessibility blind CLIP fairness low vision multi-modal vision-language zero-shot classification

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DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks

DocRes: A Generalist Model Toward Unifying Document Image Re...

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

作者： Zhang, Jiaxin Peng, Dezhi Liu, Chongyu Zhang, Peirong Jin, Lianwen South China Univ Technol Guangzhou Peoples R China INTSIG SCUT Joint Lab Document Anal & Recognit Beijing Peoples R China

ISBN: (纸本)9798350353006

Document image restoration is a crucial aspect of Document AI systems, as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently, leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge, we propose DocRes, a generalist model that unifies five document image restoration tasks including dewarping, deshadowing, appearance enhancement, deblurring, and binarization. To instruct DocRes to perform various restoration tasks, we propose a novel visual prompt approach called Dynamic Task-Specific Prompt ( DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features, which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution, DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover, DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://***/ZZZHANG-jx/DocRes.

关键词： document analysis and recognition document image enhancement unified model

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CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place recognition

CricaVPR: Cross-image Correlation-aware Representation Learn...

引用

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

作者： Lu, Feng Lan, Xiangyuan Zhang, Lijun Jiang, Dongmei Wang, Yaowei Yuan, Chun Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350353006

Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations. These networks typically produce a global representation of a place image using only this image itself and neglect the cross-image variations (e.g. viewpoint and illumination), which limits their robustness in challenging scenes. In this paper, we propose a robust global representation method with cross-image correlation awareness for VPR, named CricaVPR. Our method uses the attention mechanism to correlate multiple images within a batch. These images can be taken in the same place with different conditions or viewpoints, or even captured from different places. Therefore, our method can utilize the cross-image variations as a cue to guide the representation learning, which ensures more robust features are produced. To further facilitate the robustness, we propose a multi-scale convolution-enhanced adaptation method to adapt pre-trained visual foundation models to the VPR task, which introduces the multi-scale local information to further enhance the cross-image correlation-aware representation. Experimental results show that our method outperforms state-of-the-art methods by a large margin with significantly less training time. The code is released at https://***/Lu-Feng/CricaVPR.

关键词： convolution adapter cross-image correlation visual geo-localization visual place recognition

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TransNeXt: Robust Foveal Visual Perception for vision Transformers

TransNeXt: Robust Foveal Visual Perception for Vision Transf...

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

作者： Shi, Dai

ISBN: (纸本)9798350353006

Due to the depth degradation effect in residual connections, many efficient vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing, leading to unnatural visual perception. To address this issue, in this paper, we propose Aggregated Attention, a biomimetic design-based token mixer that simulates biological foveal vision and continuous eye movement while enabling each token on the feature map to have a global perception. Furthermore, we incorporate learnable tokens that interact with conventional queries and keys, which further diversifies the generation of affinity matrices beyond merely relying on the similarity between queries and keys. Our approach does not rely on stacking for information exchange, thus effectively avoiding depth degradation and achieving natural visual perception. Additionally, we propose Convolutional GLU, a channel mixer that bridges the gap between GLU and SE mechanism, which empowers each token to have channel attention based on its nearest neighbor image features, enhancing local modeling capability and model robustness. We combine aggregated attention and convolutional GLU to create a new visual backbone called TransNeXt. Extensive experiments demonstrate that our TransNeXt achieves state-of-the-art performance across multiple model sizes. At a resolution of 224(2), TransNeXt-Tiny attains an ImageNet accuracy of 84.0%, surpassing ConvNeXt-B with 69% fewer parameters. Our TransNeXt-Base achieves an ImageNet accuracy of 86.2% and an ImageNet-A accuracy of 61.6% at a resolution of 384(2), a COCO object detection mAP of 57.1, and an ADE20K semantic segmentation mIoU of 54.7.

关键词： Aggregated Attention Biomimetic vision Design Convolutional GLU Efficient Transformer Foveal Visual Perception Image Classification Image Segmentation ImageNet-1K ImageNet-Adversarial Large-Kernel Convolution Length-Scaled Cosine Attention Multi-Scale Extrapolation Object Detection Perceptual Artifacts Pixel-focused Attention Robustness Self-Attention vision Transformer Visual Backbone

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Segment Any Event Streams via Weighted Adaptation of Pivotal Tokens

Segment Any Event Streams via Weighted Adaptation of Pivotal...

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

作者： Chen, Zhiwen Zhu, Zhiyu Zhang, Yifan Hou, Junhui Shi, Guangming Wu, Jinjian Xidian Univ Xian Shaanxi Peoples R China City Univ Hong Kong Hong Kong Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robust and universal object segmentation within the event-centric domain. One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery. Capitalizing on the vast repositories of datasets with paired events and RGB images, our proposition is to harness and extrapolate the profound knowledge encapsulated within the pretrained SAM framework. As a cornerstone to achieving this, we introduce a multi-scale feature distillation methodology. This methodology rigorously optimizes the alignment of token embeddings originating from event data with their RGB image counterparts, thereby preserving and enhancing the robustness of the overall architecture. Considering the distinct significance that token embeddings from intermediate layers hold for higher-level embeddings, our strategy is centered on accurately calibrating the pivotal token embeddings. This targeted calibration is aimed at effectively managing the discrepancies in high-level embeddings originating from both the event and image domains. Extensive experiments on different datasets demonstrate the effectiveness of the proposed distillation method. Code in https://***/happychenpipi/EventSAM.

关键词： cross-modal knowledge distillation event-based vision segmentation

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Lookahead Exploration with Neural Radiance Representation for Continuous vision-Language Navigation

Lookahead Exploration with Neural Radiance Representation fo...

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

作者： Wang, Zihan Li, Xiangyang Yang, Jiahao Liu, Yeqi Hu, Junjie Jiang, Ming Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA Indiana Univ Dept Human Ctr Comp Indianapolis IN 46204 USA

ISBN: (纸本)9798350353006

vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step, the agent selects from possible candidate locations and then makes the move. For better navigation planning, the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end, some existing works predict RGB images for future environments, while this strategy suffers from image distortion and high computational cost. To address these issues, we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments, which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore, with the predicted future environmental representations, our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method. The code is available at https://***/MrZihan/HNR-VLN

关键词： Lookahead Exploration Neural Radiance Fields vision-and-Language Navigation

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