  Weakly-Supervised Temporal Action Localization (WS-TAL) task aims to
recognize and localize temporal starts and ends of action instances in an
untrimmed video with only video-level label supervision. Due to lack of
negative samples of background category, it is difficult for the network to
separate foreground and background, resulting in poor detection performance. In
this report, we present our 2021 HACS Challenge - Weakly-supervised Learning
Track solution that based on BaSNet to address above problem. Specifically, we
first adopt pre-trained CSN, Slowfast, TDN, and ViViT as feature extractors to
get feature sequences. Then our proposed Local-Global Background Modeling
Network (LGBM-Net) is trained to localize instances by using only video-level
labels based on Multi-Instance Learning (MIL). Finally, we ensemble multiple
models to get the final detection results and reach 22.45% mAP on the test set
