Semi-Supervised Temporal Action Proposal Generation via Exploiting 2-D Proposal Map
Weining Wang, Tianwei Lin, Dongliang He, Fu Li, Shilei Wen, Liang Wang, Jing Liu · IEEE Transactions on Multimedia · 2021
Temporal action proposal generation aims to generate temporal video segments containing human actions in untrimmed videos, which is always a preliminary for such video understanding tasks as action localization and temporally description grounding,etc. Fully-supervised solutions, though proven to be effective, suffer much from heavy data annotation overhead. To address this problem, this paper focuses on a rarely investigated yet practical problem of semi-supervised learning for temporal action proposal generation. Firstly, we propose aProposal Map oriented Mean-Teacher(PM-MT) model, which can use both labeled and unlabeled data for end-to-end model training. Secondly, aSuppression-and-Re-Generation(SRG) strategy is designed to generate high-quality pseudo labels for unlabeled data, which are then used to finetune the model. Extensive experiments demonstrate the effectiveness of our proposed method, by achieving the state-of-the-art results on two public benchmark datatsets on the task of semi-supervised action proposal generation and outperforming fully-supervised learning methods with only a portion of labeled data.