Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos
Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
We address the problem of temporal sentence localization in videos (TSLV).Traditional methods follow a top-down framework which localizes the target segment with predefined segment proposals.Although they have achieved decent performance, the proposals are handcrafted and redundant.Recently, bottom-up framework attracts increasing attention due to its superior efficiency.It directly predicts the probabilities for each frame as a boundary.However, the performance of bottom-up model is inferior to the top-down counterpart as it fails to exploit the segmentlevel interaction.In this paper, we propose an Adaptive Proposal Generation Network (APGN) to maintain the segment-level interaction while speeding up the efficiency.Specifically, we first perform a foregroundbackground classification upon the video and regress on the foreground frames to adaptively generate proposals.In this way, the handcrafted proposal design is discarded and the redundant proposals are decreased.Then, a proposal consolidation module is further developed to enhance the semantic of the generated proposals.Finally, we locate the target moments with these generated proposals following the top-down framework.Extensive experiments on three challenging benchmarks show that our proposed APGN significantly outperforms previous state-of-the-art methods.