Self-supervised Learning for Semi-supervised Temporal Language Grounding

• URL: http://arxiv.org/abs/2109.11475v1
• Date: Thu, 23 Sep 2021 16:29:16 GMT
• Title: Self-supervised Learning for Semi-supervised Temporal Language Grounding
• Authors: Fan Luo, Shaoxiang Chen, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang
• Abstract summary: Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video. Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance. To achieve good performance with limited annotations, we tackle this task in a semi-supervised way and propose a unified Semi-supervised Temporal Language Grounding (STLG) framework.
• Score: 84.11582376377471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video. TLG is inherently a challenging task, as it requires to have comprehensive understanding of both video contents and text sentences. Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance. To achieve good performance with limited annotations, we tackle this task in a semi-supervised way and propose a unified Semi-supervised Temporal Language Grounding (STLG) framework. STLG consists of two parts: (1) A pseudo label generation module that produces adaptive instant pseudo labels for unlabeled data based on predictions from a teacher model; (2) A self-supervised feature learning module with two sequential perturbations, i.e., time lagging and time scaling, for improving the video representation by inter-modal and intra-modal contrastive learning. We conduct experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets and the results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods with only a small portion of temporal annotations.

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