Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

Abstract Zero-Shot Learning (ZSL) has rapidly advanced in recent years. Towards
overcoming the annotation bottleneck in the Dynamic Hand Gesture Recognition (DHGR) …

Skeleton-based zero-shot action recognition aims to recognize unknown human actions
based on the learned priors of the known skeleton-based actions and a semantic descriptor …

Zero-shot skeleton-based action recognition aims to recognize actions of unseen categories
after training on data of seen categories. The key is to build the connection between visual …

Existing zero-shot skeleton-based action recognition methods utilize projection networks to
learn a shared latent space of skeleton features and semantic embeddings. The inherent …

Human pose analysis has garnered significant attention within both the research community
and practical applications, owing to its expanding array of uses, including gaming, video …

Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen
generalization with prior knowledge from only seen classes and shared semantics. Existing …

While remarkable progress has been made on supervised skeleton-based action
recognition the challenge of zero-shot recognition remains relatively unexplored. In this …

This paper presents Action-SGFA, a novel action feature alignment approach to learn unified
joint embeddings across four action modalities incorporating scene graph (SG) …

Sign Language Recognition (SLR) is a challenging research area in computer vision. To
tackle the annotation bottleneck in SLR, we formulate the problem of Zero-Shot Sign …