Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Abstract Contrastive Vision-Language Pre-training, known as CLIP, has provided a new
paradigm for learning visual representations using large-scale image-text pairs. It shows …

Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

Few-shot classification is a challenging problem as only very few training examples are
given for each new task. One of the effective research lines to address this challenge …

Novel classes frequently arise in our dynamically changing world, eg, new users in the
authentication system, and a machine learning model should recognize new classes without …

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for
learning visual representations by using large-scale contrastive image-text pairs. It shows …

Few-shot object detection, which aims at detecting novel objects rapidly from extremely few
annotated examples of previously unseen classes, has attracted significant research interest …

Emerging interests have been brought to recognize previously unseen objects given very
few training examples, known as few-shot object detection (FSOD). Recent researches …

In recent years, artificial intelligence (AI) has shown great promise in medicine. However,
explainability issues make AI applications in clinical usages difficult. Some research has …