Refining Proposals with Neighboring Contexts for Temporal Action Detection

While many methods have been proposed for generating temporal proposals, the performance of existing temporal detection pipelines is still limited by the quality of proposals. In this paper, we introduce a new refining model for temporal action detection, which incorporates the evaluation of Intersection-over-Union (IoU) value into the action classification, and then regresses a better segment using the neighboring contexts of one candidate proposal. To refine one candidate proposal, we augment it with two neighboring proposals of equal length, which capture the contextual information from the past and future segments. After extracting regional features for the augmented proposals, we utilize the dilated convolutions for contextual modeling to regress the offsets between the candidate proposal and the target segment within this augmented area. Extensive experiments on THUMOS14 demonstrate that our method successfully refines the generated proposals and achieves superior detection performance over other methods.