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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DaViT: Dual Attention Vision Transformers,ge, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial loca

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,Few-Shot Class-Incremental Learning via?Entropy-Regularized Data-Free Replay,l the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results

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,Anti-retroactive Interference for?Lifelong Learning,proposed learning paradigm can make the models of different tasks converge to the same optimum. The proposed method is validated on the MNIST, CIFAR100, CUB200 and ImageNet100 datasets. The code is available at ..

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,Towards Calibrated Hyper-Sphere Representation via?Distribution Overlap Coefficient for?Long-Tailedth classifier weights. Furthermore, a novel post-training calibration algorithm is devised to zero-costly boost the performance via inter-class overlap coefficients. Our method outperforms previous work with a large margin and achieves state-of-the-art performance on long-tailed image classification