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Sequential Modeling Enables Scalable Learning for Large vision Models

Sequential Modeling Enables Scalable Learning for Large Visi...

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

作者： Bail, Yutong Geng, Xinyang Mangalam, Karttikeya Bar, Amir Yuille, Alan L. Darrell, Trevor Malik, Jitendra Efros, Alexei A. UC Berkeley BAIR Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We introduce a novel sequential modeling approach which enables learning a Large vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions with-out needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion to-kens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time.

关键词： pretraining scaling Self-supervised Learning

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NORPPA: NOvel Ringed seal re-identification by Pelage pattern Aggregation

NORPPA: NOvel Ringed seal re-identification by Pelage Patter...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Nepovinnykh, Ekaterina Eerola, Tuomas Kalviainen, Heikki Chelak, Ilia Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Dept Computat Engn Comp Vis & Pattern Recognit Lab CVPRL Lappeenranta Finland Univ Helsinki Dept Comp Sci Fac Sci Helsinki Finland

ISBN: (纸本)9798350370287;9798350370713

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

关键词： Animals

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NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

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

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Wu, Zongwei Zhou, Zhuyun Chen, Cailian Zhou, Han Timofte, Radu Dong, Wei Tian, Yuqiong Chen, Jun Lul, Xin Zhu, Yurui Wang, Xi Li, Dong Xiao, Jie Zhang, Yunpeng Fu, Xueyang Zha, Zheng-Jun Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Wei, Yanyan Xiaol, Jie Ful, Xueyang Zhal, Zheng-Jun Lu, Xin Zhao, Zhihao Sun, Long Yang, Tingting Pan, Jinshan Tang, Jinhui Dong, Jiangxin Benjdira, Bilel Nassif, Mohammed Koubaa, Anis Elhayek, Ahmed Ali, Anas M. Tokoro, Kyotaro Kawai, Kento Yokoyama, Kaname Seno, Takuya Kondo, Yuki Ukita, Norimichi Li, Chenghua Yang, Bo Wu, Zhiqi Chen, Gao Yu, Yihan Chen, Sixiang Mane, Kai Ye, Tian Zou, Wenbin Lin, Yunlong Xing, Zhaohu Bai, Jinbin Chai, Wenhao Zhu, Lei Maheshwari, Ritik Verma, Rakshank Tekchandanil, Rahul Hambarde, Praful Tazil, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Lee, Jaeho Kim, Seongwan Sharif, S. M. A. Khujaev, Nodirkhuja Tsoy, Roman Gao, Fan Yan, Weidan Shao, Wenze Zhang, Dengyin Chen, Bin Zhang, Siqi Qian, Yanxin Chen, Yuanbin Zhou, Yuanbo Tong, Tong Wei, Rongfeng Sun, Ruiqi Liu, Yue Akalwadi, Nikhil Joshi, Amogh Malagi, Sampada Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Dukre, Adinath Deshmukh, Vivek Phutke, Shruti S. Kulkarni, Ashutosh Gonde, Anil Karthik, Arun K. Manasa, N. Priyal, Shri Hari Hao, Wei Yan, Xingzhuo Fu, Minghan Univ Wurzburg Comp Vis Lab IFI & CAIDAS Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Sci & Technol China Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Prince Muqrin Univ Artificial Intelligence Dept Medinah 41311 Saudi Arabia Toyota Technol Inst Intelligent Informat Media Lab Nagoya Japan Nanjing Artificial Intelligence Res IA AiRiA Nanjing Peoples R China Nanjing Normal Univ High Sch Jiangning Campus Nanjing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China South China Univ Technol Guangzhou Peoples R China Xiamen Univ Xiamen Peoples R China Natl Univ Singapore Singapore Singapore Univ Washington Seattle WA 98195 USA GEC Ajmer Kiranipura India CVPR Lab IIT Ropar Rupnagar India SCSS Trinity Coll Dublin Dublin Ireland Opt AI Seoul South Korea Nanjing Univ Posts & Telecommun Nanjing Peoples R China Fuzhou Univ Fuzhou Peoples R China Univ Hong Kong Logist & Supply Chain MultiTech R&D Ctr Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India KLE Technol Univ Sch Elect & Commun Engn Hubballi Karnataka India KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India Univ Teknol Malaysia Malaysia Japan Int Inst Technol MMT Kuala Lumpur Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia Shri Guru Gobind Singhji Inst Engn & Technol Nanded India Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland Shiv Nadar Univ Sch Engn Chennai Tamil Nadu India Fortinet Inc Sunnyvale CA USA Bosch Investment Ltd Shanghai Peoples R China Univ Saskatchewan Saskatoon

ISBN: (纸本)9798350365474

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： lighting removal restoration shadow

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ViT-CoMer: vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions

ViT-CoMer: Vision Transformer with Convolutional Multi-scale...

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

作者： Xia, Chunlong Wang, Xinliang Lv, Feng Hao, Xin Shi, Yifeng Baidu Inc Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Although vision Transformer (ViT) has achieved significant success in computer vision, it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems, which introduce additional pre-training costs. Therefore, we present a plain, pre-training-free, and feature-enhanced ViT back-bone with Convolutional Multi-scale feature interaction, named ViT-CoMer, which facilitates bidirectional interaction between CNN and transformer. Compared to the state-of-the-art, ViT-CoMer has the following advantages: (1) We inject spatial pyramid multi-receptive field convolutional features into the ViT architecture, which effectively alleviates the problems of limited local information interaction and single-feature representation in ViT. (2) We propose a simple and efficient CNN-Transformer bidirectional fusion interaction module that performs multi-scale fusion across hierarchical features, which is beneficial for handling dense prediction tasks. (3) We evaluate the performance of ViT-CoMer across various dense prediction tasks, different frameworks, and multiple advanced pre-training. Notably, our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data, and 62.1% mIoU on ADE20K val, both of which are comparable to state-of-the-art methods. We hope ViT-CoMer can serve as a new backbone for dense prediction tasks to facilitate future research. The code will be released at https://***/Traffic-X/ViT-CoMer.

关键词： DensePrediction visionFoundationBackbone visionTransformer

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BigGait: Learning Gait Representation You Want by Large vision Models

BigGait: Learning Gait Representation You Want by Large Visi...

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

作者： Ye, Dingqiang Fan, Chao Ma, Jingzhe Liu, Xiaoming Yu, Shiqi Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350353013;9798350353006

Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However, existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations like silhouette sequences, which inevitably introduce expensive annotation costs and potential error accumulation. Escaping from this trend, this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large vision Models (LVMs) and proposes a simple yet efficient gait framework, termed BigGait. Specifically, the Gait Representation Extractor (GRE) within BigGait draws upon design principles from established gait representations, effectively transforming all-purpose knowledge into implicit gait representations without requiring third-party supervision signals. Experiments on CCPG, CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both within-domain and cross-domain tasks in most cases, and provides a more practical paradigm for learning the next-generation gait representation. Finally, we delve into prospective challenges and promising directions in LVMs-based gait recognition, aiming to inspire future work in this emerging topic. The source code is available at https://github. com/ShiqiYu/OpenGait.

关键词： Gait recognition Large vision Models Person Re-Identification

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Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration

Dynamic Cues-Assisted Transformer for Robust Point Cloud Reg...

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

作者： Chen, Hong Yan, Pei Xiang, Sihe Tan, Yihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Hubei Engn Res Ctr Machine Vision & Intelligent S Wuhan Peoples R China

ISBN: (纸本)9798350353006

Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have predominantly embraced a coarse-to-fine matching mechanism, with the key to matching the superpoints located in patches with interframe consistent structures. However, previous methods still face challenges with ambiguous matching, because the interference information aggregated from irrelevant regions may disturb the capture of interframe consistency relations, leading to wrong matches. To address this issue, we propose Dynamic Cues-Assisted Transformer (DCATr). Firstly, the interference from irrelevant regions is greatly reduced by constraining attention to certain cues, i.e., regions with highly correlated structures of potential corresponding superpoints. Secondly, cues-assisted attention is designed to mine the inter-frame consistency relations, while more attention is assigned to pairs with high consistent confidence in feature aggregation. Finally, a dynamic updating fashion is proposed to facilitate mining richer consistency information, further improving aggregated features' distinctiveness and relieving matching ambiguity. Extensive evaluations on indoor and outdoor standard benchmarks demonstrate that DCATr outperforms all state-of-the-art methods.

关键词： ambiguous feature matching Point cloud registration transformer

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SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in vision-Language Models with Counterfactual Examples

SocialCounterfactuals: Probing and Mitigating Intersectional...

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

作者： Howard, Phillip Madasu, Avinash Le, Tiep Moreno, Gustavo Lujan Bhiwandiwalla, Anahita Lal, Vasudev Intel Labs Santa Clara CA 95052 USA

ISBN: (纸本)9798350353006

While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections between social attributes. This could be due to the difficulty of collecting an exhaustive set of image-text pairs for various combinations of social attributes. To address this challenge, we employ text-to-image diffusion models to produce counterfactual examples for probing intersectional social biases at scale. Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e.g., a given occupation) while differing only in their depiction of intersectional social attributes (e.g., race & gender). Through our over-generate-then-filter methodology, we produce SocialCounterfactuals, a high-quality dataset containing 171k image-text pairs for probing intersectional biases related to gender, race, and physical characteristics. We conduct extensive experiments to demonstrate the usefulness of our generated dataset for probing and mitigating intersectional social biases in state-of-the-art VLMs.

关键词： counterfactuals Fairness intersectionality social bias vision-language models

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On the Faithfulness of vision Transformer Explanations

On the Faithfulness of Vision Transformer Explanations

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

作者： Wu, Junyi Kang, Weitai Tang, Hao Hong, Yuan Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Connecticut Dept Comp Sci Storrs CT USA

ISBN: (纸本)9798350353006

To interpret vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps. However, whether these interpretations reflect true rationales behind the model's output is still underexplored. To address this gap, we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithfulness, we introduce Salience-guided Faithfulness Coefficient (SaCo), a novel evaluation metric leveraging essential information of salience distribution. Specifically, we con-duct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores, resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution, thereby failing to capture the faithfulness property. In contrast, our pro-posed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations. Furthermore, our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation, shedding light on potential paths for advancing vision Transformer explainability.

关键词： Explainability Transformer

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ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images

ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-vie...

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

作者： Jeong, Jinseo Koo, Junseo Zhang, Qimeng Kim, Gunhee Seoul Natl Univ Seoul South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Existing NeRF-based inverse rendering methods suppose that scenes are exclusively illuminated by distant light sources, neglecting the potential influence of emissive sources within a scene. In this work, we confront this limitation using LDR multi-view images captured with emissive sources turned on and off. Two key issues must be addressed: 1) ambiguity arising from the limited dynamic range along with unknown lighting details, and 2) the expensive computational cost in volume rendering to backtrace the paths leading to final object colors. We present a novel approach, ESR-NeRF, leveraging neural networks as learnable functions to represent ray-traced fields. By training networks to satisfy light transport segments, we regulate outgoing radiances, progressively identifying emissive sources while being aware of reflection areas. The results on scenes encompassing emissive sources with various properties demonstrate the superiority of ESR-NeRF in qualitative and quantitative ways. Our approach also extends its applicability to the scenes devoid of emissive sources, achieving lower CD metrics on the DTU dataset.

关键词： Inverse-rendering NeRF Scene-editing vision + graphics

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Active Open-Vocabulary recognition: Let Intelligent Moving Mitigate CLIP Limitations

Active Open-Vocabulary Recognition: Let Intelligent Moving M...

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

作者： Fan, Lei Zhou, Jianxiong Xing, Xiaoying Wu, Ying Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9798350353006

Active recognition, which allows intelligent agents to explore observations for better recognition performance, serves as a prerequisite for various embodied AI tasks, such as grasping, navigation and room arrangements. Given the evolving environment and the multitude of object classes, it is impractical to include all possible classes during the training stage. In this paper, we aim at advancing active open-vocabulary recognition, empowering embodied agents to actively perceive and classify arbitrary objects. However, directly adopting recent open-vocabulary classification models, like Contrastive Language Image Pretraining (CLIP), poses its unique challenges. Specifically, we observe that CLIP's performance is heavily affected by the viewpoint and occlusions, compromising its reliability in unconstrained embodied perception scenarios. Further, the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues, we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features, without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset, the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition, without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method.

关键词： Active vision Embodied perception

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