Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding

• URL: http://arxiv.org/abs/2403.11463v2
• Date: Tue, 14 May 2024 17:34:46 GMT
• Title: Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding
• Authors: Chaolei Tan, Jianhuang Lai, Wei-Shi Zheng, Jian-Fang Hu,
• Abstract summary: Video paragraph grounding aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. Existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. We introduce and explore Weakly-Supervised Video paragraph Grounding (WSVPG) to eliminate the need of temporal annotations.
• Score: 70.31050639330603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Paragraph Grounding (VPG) is an emerging task in video-language understanding, which aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. However, existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work, we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the need of temporal annotations. Different from previous weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking, we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically, we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Augmentation Branch is utilized for directly regressing the temporal boundaries of a complete paragraph within a pseudo video, and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multiple sentences in a normal video. We demonstrate by extensive experiments that our paradigm has superior practicability and flexibility to achieve efficient weakly-supervised or semi-supervised learning, outperforming state-of-the-art methods trained with the same or stronger supervision.

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