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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,Meta-Learning with?Less Forgetting on?Large-Scale Non-Stationary Task Distributions,tributions sequentially arrive with some ORDER), to tackle these two major challenges. Specifically, our ORDER introduces a novel mutual information regularization to robustify the model with unlabeled OOD data and adopts an optimal transport regularization to remember previously learned knowledge i

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,DnA: Improving Few-Shot Transfer Learning with?Low-Rank Decomposition and?Alignment,the low-rank subspace), and the extra flexibility to absorb the new out-of-the-domain knowledge (via freeing the sparse residual). Our resultant framework, termed Decomposition-and-Alignment (.), significantly improves the few-shot transfer performance of the SS pre-trained model to downstream tasks

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,Open-World Semantic Segmentation via?Contrasting and?Clustering Vision-Language Embedding,mage encoder is jointly trained with a vision-based contrasting and a cross-modal contrasting, which encourage the visual embeddings to preserve both fine-grained semantics and high-level category information that are crucial for the segmentation task. Furthermore, an online clustering head is devis

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