Annotation-Efficient Hybrid Learning for Temporal Sentence Grounding

Temporal Sentence Grounding (TSG) aims at localizing a temporal interval in an untrimmed video that contains the most relevant semantics to a given query sentence. Most existing methods either focus on addressing the problem in a fully-supervised manner where the temporal boundary annotations are provided, or are dedicated to weakly-supervised TSG without any boundary annotations. However, the former ones suffer from expensive annotation cost and the latter ones only give inferior grounding performance. In this paper, we propose an Annotation-efficient Hybrid Learning (AHL) framework that aims to achieve good TSG performance with less annotation cost by leveraging weakly semi-supervised learning, contrastive learning and active learning: (1) AHL includes a progressive pseudo-label self-learning module which generates pseudo labels and progressively selects reliable ones to re-train the model in a progressive manner; (2) AHL includes a novel self-guided contrastive learning method that performs proposal-level contrastive learning based on weakly-labeled data to align the visual and language feature; (3) AHL explores the fully-labeled set construction by gradually expanding it via actively searching on the informative weakly-labeled samples, from the aspects of both difficulty and diversity. We conduct extensive experiments on ActivityNet and Charades-STA datasets and results verify the effectiveness of our proposed AHL to exploit the weakly-labeled data and to achieve the same performance as fully-supervised method, with much less annotation cost. Our code is available at https://github.com/DJX1995/AHL.