Temporal Relation-Aware Global Attention Network for Temporal Action Detection

Temporal Action Detection (TAD) is a crucial task in video understanding. Its primary objective is to accurately identify the semantic labels of each action instance in an untrimmed video, along with their temporal range. This paper constructs the Temporal Relation-aware Global Attention Network (TRGA-Net), which is a long-term temporal context modelling network. The model comprises video preprocessing, spatiotemporal feature extraction, temporal context modelling, and temporal action detection header. TRGA-Net introduces a temporal context modelling-based temporal channel global attention module to efficiently perform long-term temporal context modelling. Experiments were conducted on the ActivityNet and THUMOS14 datasets to evaluate the performance of TRGA-Net. The results demonstrate better mean average precision (mAP) metrics than the previously proposed temporal detection model, verifying the usefulness of TRGA-Net for temporal context modelling.