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 …

Classification of human actions is an ongoing research problem in computer vision. This
review is aimed to scope current literature on data fusion and action recognition techniques …

We present CLIP2Video network to transfer the image-language pre-training model to video-
text retrieval in an end-to-end manner. Leading approaches in the domain of video-and …

Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

In this work, we combine 3D convolution with late temporal modeling for action recognition.
For this aim, we replace the conventional Temporal Global Average Pooling (TGAP) layer at …

Abstract Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and
localize all action instances in an untrimmed video under only video-level supervision …

Word-level sign language recognition (WSLR) is a fundamental task in sign language
interpretation. It requires models to recognize isolated sign words from videos. However …

In recent years, deep learning has been widely used in breast cancer diagnosis, and many
high-performance models have emerged. However, most of the existing deep learning …

Skeleton sequences are lightweight and compact and thus are ideal candidates for action
recognition on edge devices. Recent skeleton-based action recognition methods extract …