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 …

In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

Learning from a limited number of samples is challenging since the learned model can
easily become overfitted based on the biased distribution formed by only a few training …

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …

This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied
setting for anomaly detection (AD), where only a limited number of normal images are …

Few-shot object detection is an imperative and long-lasting problem due to the inherent long-
tail distribution of real-world data. Its performance is largely affected by the data scarcity of …

The recent flourish of deep learning in various tasks is largely accredited to the rich and
accessible labeled data. Nonetheless, massive supervision remains a luxury for many real …

Recent progress on few-shot learning largely relies on annotated data for meta-learning:
base classes sampled from the same domain as the novel classes. However, in many …

Current few-shot action recognition methods reach impressive performance by learning
discriminative features for each video via episodic training and designing various temporal …