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This technical report presents an overview of our solution used in thesubmission to ActivityNet Challenge 2020 Task 1 (\textbf{temporal actionlocalization/detection}). Temporal action localization requires to not onlyprecisely locate the temporal boundaries of action instances, but alsoaccurately classify the untrimmed videos into specific categories. In thispaper, we decouple the temporal action localization task into two stages (i.e.proposal generation and classification) and enrich the proposal diversitythrough exhaustively exploring the influences of multiple components fromdifferent but complementary perspectives. Specifically, in order to generatehigh-quality proposals, we consider several factors including the video featureencoder, the proposal generator, the proposal-proposal relations, the scaleimbalance, and ensemble strategy. Finally, in order to obtain accuratedetections, we need to further train an optimal video classifier to recognizethe generated proposals. Our proposed scheme achieves the state-of-the-artperformance on the temporal action localization task with \textbf{42.26}average mAP on the challenge testing set.

Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020