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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-means Mask Transformer, as a clustering process. Inspired by the traditional .-means clustering algorithm, we develop a .-means .sk .former (.MaX-DeepLab) for segmentation tasks, which not only improves the state-of-the-art, but also enjoys a simple and elegant design. As a result, our .MaX-DeepLab achieves a new state-of

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,SegPGD: An Effective and?Efficient Adversarial Attack for?Evaluating and?Boosting Segmentation Robuk, we propose an effective and efficient segmentation attack method, dubbed SegPGD. Besides, we provide a convergence analysis to show the proposed SegPGD can create more effective adversarial examples than PGD under the same number of attack iterations. Furthermore, we propose to apply our SegPGD a

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,Adversarial Erasing Framework via?Triplet with?Gated Pyramid Pooling Layer for?Weakly Supervised Sethey usually suffer from the over-expansion due to an absence of guidelines on when to stop erasing. We experimentally verify that the over-expansion is due to rigid classification, and metric learning can be a flexible remedy for it. AEFT is devised to learn the concept of erasing with the triplet