Human skeleton, as a compact representation of human action, has received increasing
attention in recent years. Many skeleton-based action recognition methods adopt GCNs to …

Human action recognition (HAR) in RGB-D videos has been widely investigated since the
release of affordable depth sensors. Currently, unimodal approaches (eg, skeleton-based …

In vision-based action recognition, spatio-temporal features from different modalities are
used for recognizing activities. Temporal modeling is a long challenge of action recognition …

Skeleton-based action segmentation requires recognizing composable actions in untrimmed
videos. Current approaches decouple this problem by first extracting local visual features …

An important challenge in vision-based action recognition is the embedding of
spatiotemporal features with two or more heterogeneous modalities into a single feature. In …

The goal of building a benchmark (suite of datasets) is to provide a unified protocol for fair
evaluation and thus facilitate the evolution of a specific area. Nonetheless, we point out that …

While recognition accuracies of video classification models trained on conventional
benchmarks are gradually saturating, recent studies raise alarm about the learned …

Humans are remarkably flexible in understanding viewpoint changes due to visual cortex
supporting the perception of 3D structure. In contrast, most of the computer vision models …

Skeleton-based methods have made remarkable strides in human action recognition (HAR).
However, the performance of existing unimodal approaches is still limited by the lack of …

Abstract Vision-based Human Action Recognition (HAR) is a hot topic in computer vision.
Recently, deep-based HAR has shown promising results. HAR using a single data modality …