Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

脾氣暴躁的人

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Fortuitous

,tSF: Transformer-Based Semantic Filter for?Few-Shot Learning,een (novel) labeled samples. Most feature embedding modules in recent FSL methods are specially designed for corresponding learning tasks (e.g., classification, segmentation, and object detection), which limits the . of embedding features. To this end, we propose a light and universal module named t

ironic

,Adversarial Feature Augmentation for?Cross-domain Few-Shot Classification,sting methods based on meta-learning predict novel-class labels for (target domain) testing tasks via meta knowledge learned from (source domain) training tasks of base classes. However, most existing works may fail to generalize to novel classes due to the probably large domain discrepancy across d

headlong

,Constructing Balance from?Imbalance for?Long-Tailed Image Recognition,ail) classes severely skews the data-driven deep neural networks. Previous methods tackle with data imbalance from the viewpoints of data distribution, feature space, and model design, etc. In this work, instead of directly learning a recognition model, we suggest confronting the bottleneck of head-

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,On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and?Beyond,om the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addre

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單調(diào)性

annexation

,Exploring Hierarchical Graph Representation for?Large-Scale Zero-Shot Image Classification,ands of categories as in the ImageNet-21K benchmark. At this scale, especially with many fine-grained categories included in ImageNet-21K, it is critical to learn quality visual semantic representations that are discriminative enough to recognize unseen classes and distinguish them from seen ones. W

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Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation, task, the main challenge is how to accurately measure the semantic correspondence between the support and query samples with limited training data. To address this problem, we propose to aggregate the learnable covariance matrices with a deformable 4D Transformer to effectively predict the segmenta