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

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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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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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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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Towards Part-Based Understanding of RGB-D Scans

Towards Part-Based Understanding of RGB-D Scans

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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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H-ViT: A Hierarchical vision Transformer for Deformable Image Registration

H-ViT: A Hierarchical Vision Transformer for Deformable Imag...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Morteza Ghahremani Mohammad Khateri Bailiang Jian Benedikt Wiestler Ehsan Adeli Christian Wachinger Technical University of Munich Munich Center for Machine Learning University of Eastern Finland Stanford University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels. As a Hierarchical vision Transformer (H- ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent low-level ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration. The project link is available at https://***/hvit.

关键词： computer vision Image registration Visualization Accuracy Deformation Transformers pattern recognition

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Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation

Differentiable SLAM-net: Learning Particle SLAM for Visual N...

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

作者： Karkus, Peter Cai, Shaojun Hsu, David Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all model components jointly for the end-objective, SLAM-net learns to be robust in challenging conditions. We run experiments in the Habitat platform with different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture with SLAMnet improves the state-of-the-art for the Habitat Challenge 2020 PointNav task by a large margin (37% to 64% success).

关键词： Visualization Simultaneous localization and mapping Navigation Robot vision systems Neural networks computer architecture Particle filters

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GenesisTex: Adapting Image Denoising Diffusion to Texture Space

GenesisTex: Adapting Image Denoising Diffusion to Texture Sp...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chenjian Gao Boyan Jiang Xinghui Li Yingpeng Zhang Qian Yu School of Software Beihang University R&D Efficiency and Capability Department Tencent IEG

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the rendering of the corresponding viewpoint. The sampled latent texture maps are then decoded into a final texture map. During the sampling process, we focus on both global and local consistency across multiple viewpoints: global consistency is achieved through the integration of style consistency mechanisms within the noise prediction network, and low-level consistency is achieved by dynamically aligning latent textures. Finally, we apply reference-based inpainting and img2img on denser views for texture refinement. Our approach overcomes the limitations of slow optimization in distillation-based methods and instability in inpainting-based methods. Experiments on meshes from various sources demonstrate that our method surpasses the baseline methods quantitatively and qualitatively.

关键词： Geometry computer vision Three-dimensional displays Noise Rendering (computer graphics) Diffusion models pattern recognition

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Progressive Temporal Feature Alignment Network for Video Inpainting

Progressive Temporal Feature Alignment Network for Video Inp...

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

作者： Zou, Xueyan Yang, Linjie Liu, Ding Lee, Yong Jae ByteDance Inc Beijing Peoples R China Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781665445092

Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolution. However, flow-based warping can create artifacts when optical flow is not accurate, while temporal convolution may suffer from spatial misalignment. We propose 'Progressive Temporal Feature Alignment Network', which progressively enriches features extracted from the current frame with the feature warped from neighbouring frames using optical flow. Our approach corrects the spatial misalignment in the temporal feature propagation stage, greatly improving visual quality and temporal consistency of the inpainted videos. Using the proposed architecture, we achieve state-of-the-art performance on the DAVIS and FVI datasets compared to existing deep learning approaches. Code is available at https://***/MaureenZOU/TSAM.

关键词： Deep learning Visualization computer vision Three-dimensional displays Image resolution Convolution Feature extraction

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Supervised Contrastive Learning for Robust and Efficient Multi-modal Emotion and Sentiment Analysis 26

Supervised Contrastive Learning for Robust and Efficient Mul...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Gomaa, Ahmed Maier, Andreas Kosti, Ronak Friedrich Alexander Univ Erlangen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Expression of human emotion and sentiment are often multi-modal consisting use of spoken speech, vision, and text. Combining multiple modalities allows learning-based models to benefit with the complementary information present across modalities to produce more accurate predictions. One of the bigger challenges in multi-modal affective computing is performance consistency in non-ideal scenarios. Most benchmarks fail to generalize in non-ideal scenarios where one of the modalities is missing or highly corrupted due to occlusion, sensor errors, or change of orientation. Consequently, various modality fusion approaches were proposed. However, most of these fusion approaches assume that each modality is equally useful. To address the challenge of performance consistency, in this work we propose to use supervised contrastive learning (SCL). We demonstrate through various experiments and comparison with state-of-the-art (SOTA) methods that the model robustness against corrupted and missing modalities improves when trained with SCL. Next, we use the Perceiver architecture[1] in order to efficiently combine the representations of different modalities. Its iterative attention mechanism allows to create a reduced latent representation in an efficient manner. We observe that it can accommodate a wide range of modality combinations, allowing for robust information fusion. Our approach allows reduction of model complexity and efficient fusion of different modalities, while maintaining the performance consistency and model robustness. We conduct ablation experiments to study the effect of each contribution in different scenarios, and we show that the proposed methods outperform the state-of-art, while simultaneously being robust to corrupted modalities. Our method also outperforms its counterparts and SOTA while using less numerical complexity (inference times and compute operations).

关键词： Training Sentiment analysis Computational modeling computer architecture Predictive models Transformers Robustness

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