Spatiotemporal human representation based on 3D visual perception data is a rapidly
growing research area. Representations can be broadly categorized into two groups …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Dynamics of human body skeletons convey significant information for human action
recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted …

Few-shot learning aims to recognize new concepts from very few examples. However, most
of the existing few-shot learning methods mainly concentrate on the first-order statistic of …

Many few-shot learning models focus on recognising images. In contrast, we tackle a
challenging task of few-shot action recognition from videos. We build on a C3D encoder for …

We investigate the localization of subtle yet discriminative parts for fine-grained image
recognition. Based on the observation that such parts typically exist within a hierarchical …

Human actions in video sequences are characterized by the complex interplay between
spatial features and their temporal dynamics. In this paper, we propose novel tensor …

This paper presents SPARE, a self-supervised part erasing framework for ultra-fine-grained
visual categorization. The key insight of our model is to learn discriminative representations …

As the prosperity of low-cost and easy-operating depth cameras, skeleton-based human
action recognition has been extensively studied recently. However, most of the existing …

Deep learning methods have achieved remarkable success in fine-grained visual
categorization. Such successful categorization at sub-ordinate level, eg, different animal or …