This technical report presents our first place winning solution for temporal
action detection task in CVPR-2022 AcitivityNet Challenge. The task aims to
localize temporal boundaries of action instances with specific classes in long
untrimmed videos. Recent mainstream attempts are based on dense boundary
matchings and enumerate all possible combinations to produce proposals. We
argue that the generated proposals contain rich contextual information, which
may benefits detection confidence prediction. To this end, our method mainly
consists of the following three steps: 1) action classification and feature
extraction by Slowfast, CSN, TimeSformer, TSP, I3D-flow, VGGish-audio, TPN and
ViViT; 2) proposal generation. Our proposed Context-aware Proposal Network
(CPN) builds on top of BMN, GTAD and PRN to aggregate contextual information by
randomly masking some proposal features. 3) action detection. The final
detection prediction is calculated by assigning the proposals with
corresponding video-level classifcation results. Finally, we ensemble the
results under different feature combination settings and achieve 45.8%
performance on the test set, which improves the champion result in CVPR-2021
ActivityNet Challenge by 1.1% in terms of average mAP.

本技术报告介绍了我们在 CVPR-2022 AcitivityNet 挑战赛中获取的第一名的解决方案，该解决方案旨在为长型未修剪视频中的特定类别的动作实例定位时间边界，并且通过提出 Context-aware Proposal Network (CPN) 等创新技术，在提高精度方面大有改进。