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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,AdvDO: Realistic Adversarial Attacks for Trajectory Prediction,her prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed . to gener

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Arteriography

One Size Does NOT Fit All: Data-Adaptive Adversarial Training,ne of the most effective ways to improve the model’s adversarial robustness, it usually yields models with lower natural accuracy. In this paper, we argue that, for the attackable examples, traditional adversarial training which utilizes a fixed size perturbation ball can create adversarial examples

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,UniCR: Universally Approximated Certified Robustness via Randomized Smoothing,ximated certified robustness (UniCR) framework, which can approximate the robustness certification of . input on . classifier against . . perturbations with noise generated by . continuous probability distribution. Compared with the state-of-the-art certified defenses, UniCR provides many significan

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archetype

,Robust Network Architecture Search via Feature Distortion Restraining,domains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust

欢笑

,SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination,ned models are released online to facilitate further research. However, it raises extensive concerns on whether these pre-trained models would leak privacy-sensitive information of their training data. Thus, in this work, we aim to answer the following questions: “Can we effectively recover private

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