Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences

arXiv:2405.04900v1 Announce Type: new
Abstract: Emotion recognition is an important part of affective computing. Extracting emotional cues from human gaits yields benefits such as natural interaction, a nonintrusive nature, and remote detection. Recently, the introduction of self-supervised learning techniques offers a practical solution to the issues arising from the scarcity of labeled data in the field of gait-based emotion recognition. However, due to the limited diversity of gaits and the incompleteness of feature representations for skeletons, the existing contrastive learning …

abstract arxiv benefits computing cs.cv detection emotion human introduction natural nature part practical recognition representation representation learning self-supervised learning solution supervised learning type