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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FOLLY

,Learning Semantic Correspondence with Sparse Annotations,stantiate our paradigm with two variants of learning strategies: a single offline teacher setting, and mutual online teachers setting. Our approach achieves notable improvements on three challenging benchmarks for semantic correspondence and establishes the new state-of-the-art. Project page: ..

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FrequencyLowCut Pooling - Plug and Play Against Catastrophic Overfitting,an image and signal processing point of view, this success might be a bit surprising as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e. . in their down-sampling operations. However, since poor sampling appeared not to affect model accuracy,

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TAFIM: Targeted Adversarial Attacks Against Facial Image Manipulations,approach that produces image-specific perturbations which are embedded in the original images. The key idea is that these protected images prevent face manipulation by causing the manipulation model to produce a predefined manipulation target (uniformly colored output image in our case) instead of t

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,FingerprintNet: Synthesized Fingerprints for Generated Image Detection, false news. To prevent such cases, vigorous research is conducted on distinguishing the generated images from the real ones, but challenges still remain with detecting the unseen generated images outside of the training settings. To overcome this problem, we analyze the distinctive characteristic o

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Harridan

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,Exploring Disentangled Content Information for Face Forgery Detection,le performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily em