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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30983 条记录，以下是4811-4820 订阅

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Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer

Diverse Part Discovery: Occluded Person Re-identification wi...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Yulin He, Jianfeng Zhang, Tianzhu Liu, Xiang Zhang, Yongdong Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Dongguan Univ Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9781665445092

Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoder-decoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder. The proposed PAT model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit the transformer encoder-decoder architecture for occluded person Re-ID in a unified deep model. Second, to learn part prototypes well with only identity labels, we design two effective mechanisms including part diversity and part discriminability. Consequently, we can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner. Extensive experimental results on six challenging benchmarks for three tasks (occluded, partial and holistic Re-ID) demonstrate that our proposed PAT performs favorably against stat-of-the-art methods.

关键词： computer vision Prototypes computer architecture Benchmark testing Transformers pattern recognition Decoding

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Plan2Scene: Converting Floorplans to 3D Scenes

Plan2Scene: Converting Floorplans to 3D Scenes

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vidanapathirana, Madhawa Wu, Qirui Furukawa, Yasutaka Chang, Angel X. Savva, Manolis Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. Our system 1) lifts a floorplan image to a 3D mesh model;2) synthesizes surface textures based on the input photos;and 3) infers textures for unobserved surfaces using a graph neural network architecture. To train and evaluate our system we create indoor surface texture datasets, and augment a dataset of floorplans and photos from prior work with rectified surface crops and additional annotations. Our approach handles the challenge of producing tileable textures for dominant surfaces such as floors, walls, and ceilings from a sparse set of unaligned photos that only partially cover the residence. Qualitative and quantitative evaluations show that our system produces realistic 3D interior models, outperforming baseline approaches on a suite of texture quality metrics and as measured by a holistic user study.

关键词： Measurement Solid modeling computer vision Three-dimensional displays Crops Graph neural networks Surface texture

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Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos

Co-Grounding Networks with Semantic Attention for Referring ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Song, Sijie Lin, Xudong Liu, Jiaying Guo, Zongming Chang, Shih-Fu Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Columbia Univ DVMM Lab New York NY USA

ISBN: (纸本)9781665445092

In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-grounding, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and LiOTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset.

关键词： Visualization computer vision Correlation Grounding Computational modeling Semantics Benchmark testing

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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

SSTVOS: Sparse Spatiotemporal Transformers for Video Object ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Duke, Brendan Ahmed, Abdalla Wolf, Christian Aarabi, Parham Taylor, Graham W. Univ Toronto Toronto ON Canada Univ Guelph Guelph ON Canada Univ Lyon INSA Lyon LIRIS Lyon France Modiface Inc Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features. Our attention-based formulation for VOS allows a model to learn to attend over a history of multiple frames and provides suitable inductive bias for performing correspondence-like computations necessary for solving motion segmentation. We demonstrate the effectiveness of attention-based over recurrent networks in the spatiotemporal domain. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness to occlusions compared with the state of the art.

关键词： computer vision Codes Runtime Scalability Computational modeling Object segmentation Transformers

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Discrete-continuous Action Space Policy Gradient-based Attention for Image-Text Matching

Discrete-continuous Action Space Policy Gradient-based Atten...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yan, Shiyang Yu, Li Xie, Yuan Nanjing Univ Informat Sci & Technol Nanjing Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely used in image-text matching models does not have supervision. We propose a novel attention scheme which projects the image and text embedding into a common space and optimises the attention weights directly towards the evaluation metrics. The proposed attention scheme can be considered as a kind of supervised attention and requiring no additional annotations. It is trained via a novel Discrete-continuous action space policy gradient algorithm, which is more effective in modelling complex action space than previous continuous action space policy gradient. We evaluate the proposed methods on two widely-used benchmark datasets: Flickr3Ok and MS-COCO, out-performing the previous approaches by a large margin.

关键词： Measurement computer vision Annotations Computational modeling Semantics Transforms Benchmark testing

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Towards Part-Based Understanding of RGB-D Scans

Towards Part-Based Understanding of RGB-D Scans

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bokhovkin, Alexey Ishimtsev, Vladislav Bogomolov, Emil Zorin, Denis Artemov, Alexey Burnaev, Evgeny Dai, Angela Tech Univ Munich Munich Germany Skolkovo Inst Sci & Technol Moscow Russia NYU New York NY 10003 USA

ISBN: (纸本)9781665445092

Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes;however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-based scene understanding of real-world 3D environments: from an RGB-D scan of a scene, we detect objects, and for each object predict its decomposition into geometric part masks, which composed together form the complete geometry of the observed object. We leverage an intermediary part graph representation to enable robust completion as well as building of part priors, which we use to construct the final part mask predictions. Our experiments demonstrate that guiding part understanding through part graph to part prior-based predictions significantly outperforms alternative approaches to the task of semantic part completion.

关键词： Geometry computer vision Three-dimensional displays Semantics Buildings Cognition Autonomous agents

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PLM: Partial Label Masking for Imbalanced Multi-label Classification

PLM: Partial Label Masking for Imbalanced Multi-label Classi...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Duarte, Kevin Rawat, Yogesh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665448994

Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative samples for each class skews network output probabilities further from ground-truth distributions. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. By stochastically masking labels during loss computation, the method balances this ratio for each class, leading to improved recall on minority classes and improved precision on frequent classes. The ratio is estimated adaptively based on the network's performance by minimizing the KL divergence between predicted and ground-truth distributions. Whereas most existing approaches addressing data imbalance are mainly focused on single-label classification and do not generalize well to the multi-label case, this work proposes a general approach to solve the long-tail data imbalance issue for multi-label classification. PLM is versatile: it can be applied to most objective functions and it can be used alongside other strategies for class imbalance. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.

关键词： Training computer vision conferences Neural networks Linear programming pattern recognition Classification algorithms

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MDFIDNet: Multi-domain Feature Integration Denoising Network 27th

MDFIDNet: Multi-domain Feature Integration Denoising Network

引用

27th International conference on pattern recognition, ICPR 2024

作者： Das, Debashis Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

ISBN: (纸本)9783031781247

In the realm of computer vision, image denoising remains a formidable challenge with profound implications for fields like medical imaging, remote sensing, and photography. Despite notable advancements in deep learning, there are enduring challenges: current convolutional neural networks (CNNs) frequently struggle with training complexities due to their emphasis on increased network depth. At the same time, these networks often fail to adequately consider the crucial role of gradient information in the denoising process. Furthermore, there is a distinct gap in leveraging transform domain analysis in image denoising. This study addresses these limitations with MDFIDNet, a novel triple-phase attentive fusion network tailored for image denoising. MDFIDNet integrates three independent feature extraction pipelines: a frequency domain processing pipeline (FDP) enhanced by a multi-scale convolutional attention Block (MSCAB), a spatial domain processing pipeline (SDP) focusing on detail feature preservation, and a gradient-domain processing pipeline (GDP) driven by multidirectional gradient information. Experimental validation demonstrates that MDFIDNet surpasses existing benchmarks, exhibiting robust performance across diverse datasets. Comprehensive ablation studies underscore the individual contributions of each network component, elucidating the novel advancements that underpin MDFIDNet’s superior denoising efficacy. The source code and further details are available in the https://***/debashis15/MDFIDNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation

Cyclic Co-Learning of Sounding Object Visual Grounding and S...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tian, Yapeng Hu, Di Xu, Chenliang Univ Rochester Rochester NY 14627 USA Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9781665445092

There are rich synchronized audio and visual events in our daily life. Inside the events, audio scenes are associated with the corresponding visual objects;meanwhile, sounding objects can indicate and help to separate their individual sounds in the audio track. Based on this observation, in this paper, we propose a cyclic co-learning (CCoL) paradigm that can jointly learn sounding object visual grounding and audio-visual sound separation in a unified framework. Concretely, we can leverage grounded object-sound relations to improve the results of sound separation. Meanwhile, benefiting from discriminative information from separated sounds, we improve training example sampling for sounding object grounding, which builds a co-learning cycle for the two tasks and makes them mutually beneficial. Extensive experiments show that the proposed framework outperforms the compared recent approaches on both tasks, and they can benefit from each other with our cyclic co-learning.

关键词： Training Visualization computer vision Codes Grounding Computational modeling pattern recognition

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U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmen...

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu Albany United States School of Physics and Electronics Shandong Normal University Jinan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Computer and Information Technology Purdue University West Lafayette United States University of North Carolina at Chapel Hill Chapel Hill United States

ISBN: (纸本)9783031818530

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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