Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

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书目名称Computer Vision – ACCV 2022影响因子(影响力)




书目名称Computer Vision – ACCV 2022影响因子(影响力)学科排名




书目名称Computer Vision – ACCV 2022网络公开度




书目名称Computer Vision – ACCV 2022网络公开度学科排名




书目名称Computer Vision – ACCV 2022被引频次




书目名称Computer Vision – ACCV 2022被引频次学科排名




书目名称Computer Vision – ACCV 2022年度引用




书目名称Computer Vision – ACCV 2022年度引用学科排名




书目名称Computer Vision – ACCV 2022读者反馈




书目名称Computer Vision – ACCV 2022读者反馈学科排名

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Lecture Notes in Computer Scienceworks for this task often rely on external tasks, ., pose estimation, or semantic segmentation, to extract local features over fixed given regions. However, these external models may perform poorly on Occluded ReID, since they are still open problems with no reliable performance guarantee and are no

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Advances in Cryptology – CRYPTO 2021its embedding space. Despite recent advances in deep metric learning, it remains challenging for the learned metric to generalize to unseen classes with a substantial domain gap. To tackle the issue, we explore a new problem of few-shot metric learning that aims to adapt the embedding function to th

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https://doi.org/10.1007/978-3-031-15985-5hat jointly detect and recognize scene text have been proposed in recent years. In this paper, we present a novel end-to-end text spotting network SPRNet for arbitrary-shaped scene text. We propose a parametric B-spline centerline-based representation model to describe the distinctive global shape c

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Allen Kim,Xiao Liang,Omkant Pandeyoxes updated in the cascaded training manner. However, due to the sparse nature and the one-to-one relation between the query and its attending region, it heavily depends on the self attention, which is usually inaccurate in the early training stage. Moreover, in a scene of dense objects, the object

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Lecture Notes in Computer Sciencence in low-resolution face images with a pixel width of 50 pixels or less. To solve a limitation under the challenging low-resolution conditions, we propose a high-frequency attentive super-resolved gaze estimation network, i.e., HAZE-Net. Our network improves the resolution of the input image and e

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