Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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




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




书目名称Computer Vision – ECCV 2022网络公开度




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




书目名称Computer Vision – ECCV 2022被引频次




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




书目名称Computer Vision – ECCV 2022年度引用




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




书目名称Computer Vision – ECCV 2022读者反馈




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

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联想记忆

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,Designing One Unified Framework for High-Fidelity Face Reenactment and Swapping,and practical-unfriendly. In this paper, we propose an effective end-to-end unified framework to achieve both tasks. Unlike existing methods that directly utilize pre-estimated structures and do not fully exploit their potential similarity, our model sufficiently transfers identity and attribute bas

covert

,Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives, kinds of signals due to its continuous, differentiable properties, showing superiorities to classical discretized representations. However, the training of neural networks for INRs only utilizes input-output pairs, and the derivatives of the target output with respect to the input, which can be acc

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Deep Bayesian Video Frame Interpolation,part. Our approach learns posterior distributions of optical flows and frames to be interpolated, which is optimized via learned gradient descent for fast convergence. Each learned step is a lightweight network manipulating gradients of the log-likelihood of estimated frames and flows. Such gradient

novelty

,Cross Attention Based Style Distribution for Controllable Person Image Synthesis,e propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross att