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Temporal action localization (TAL) aims to detect the boundary and identifythe class of each action instance in a long untrimmed video. Current approachestreat video frames homogeneously, and tend to give background and key objectsexcessive attention. This limits their sensitivity to localize actionboundaries. To this end, we propose a prior-enhanced temporal actionlocalization method (PETAL), which only takes in RGB input and incorporatesaction subjects as priors. This proposal leverages action subjects' informationwith a plug-and-play subject-aware spatial attention module (SA-SAM) togenerate an aggregated and subject-prioritized representation. Experimentalresults on THUMOS-14 and ActivityNet-1.3 datasets demonstrate that the proposedPETAL achieves competitive performance using only RGB features, e.g., boostingmAP by 2.41% or 0.25% over the state-of-the-art approach that uses RGB featuresor with additional optical flow features on the THUMOS-14 dataset.

Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention