  In this report, we present our solution for the task of temporal action
localization (detection) (task 1) in ActivityNet Challenge 2020. The purpose of
this task is to temporally localize intervals where actions of interest occur
and predict the action categories in a long untrimmed video. Our solution
mainly includes three components: 1) feature encoding: we apply three kinds of
backbones, including TSN [7], Slowfast[3] and I3d[1], which are both pretrained
on Kinetics dataset[2]. Applying these models, we can extract snippet-level
video representations; 2) proposal generation: we choose BMN [5] as our
baseline, base on which we design a Cascade Boundary Refinement Network
(CBR-Net) to conduct proposal detection. The CBR-Net mainly contains two
modules: temporal feature encoding, which applies BiLSTM to encode long-term
temporal information; CBR module, which targets to refine the proposal
precision under different parameter settings; 3) action localization: In this
stage, we combine the video-level classification results obtained by the fine
tuning networks to predict the category of each proposal. Moreover, we also
apply to different ensemble strategies to improve the performance of the
designed solution, by which we achieve 42.788% on the testing set of
ActivityNet v1.3 dataset in terms of mean Average Precision metrics.
