Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Movement dataset reviews exist but are limited in coverage, both in terms of size and
research discipline. While topic-specific reviews clearly have their merit, it is critical to have a …

Multi-view action recognition targets to integrate complementary information from different
views to improve classification performance. It is a challenging task due to the distinct gap …

Abstract We propose a Dynamic Directed Graph Convolutional Network (DDGCN) to model
spatial and temporal features of human actions from their skeletal representations. The …

Sign language is commonly used by deaf or mute people to communicate but requires
extensive effort to master. It is usually performed with the fast yet delicate movement of hand …

Most multimodal learning methods assume that all modalities are always available in data.
However, in real-world applications, the assumption is often violated due to privacy …

Gait recognition systems typically rely solely on silhouettes for extracting gait signatures.
Nevertheless, these approaches struggle with changes in body shape and dynamic …

Image retrieval is one of the key techniques of computer vision, and has been studied for a
long time. Nevertheless, little attention is paid to infrared and visible cross-modal retrieval …

Human action recognition which recognizes human actions in a video is a fundamental task
in computer vision field. Although multiple existing methods with single-view or multi-view …

Action detection plays an important role in video understanding and attracts considerable
attention in the last decade. However, current action detection methods are mainly based on …