  This technical report presents our solution for temporal action detection
task in AcitivityNet Challenge 2021. The purpose of this task is to locate and
identify actions of interest in long untrimmed videos. The crucial challenge of
the task comes from that the temporal duration of action varies dramatically,
and the target actions are typically embedded in a background of irrelevant
activities. Our solution builds on BMN, and mainly contains three steps: 1)
action classification and feature encoding by Slowfast, CSN and ViViT; 2)
proposal generation. We improve BMN by embedding the proposed Proposal Relation
Network (PRN), by which we can generate proposals of high quality; 3) action
detection. We calculate the detection results by assigning the proposals with
corresponding classification results. Finally, we ensemble the results under
different settings and achieve 44.7% on the test set, which improves the
champion result in ActivityNet 2020 by 1.9% in terms of average mAP.
