Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining

• URL: http://arxiv.org/abs/2505.06557v1
• Date: Sat, 10 May 2025 08:03:00 GMT
• Title: Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining
• Authors: Lu Dong, Haiyu Zhang, Hongjie Zhang, Yifei Huang, Zhen-Hua Ling, Yu Qiao, Limin Wang, Yali Wang,
• Abstract summary: The task of weakly supervised temporal sentence grounding (WSTSG) aims to detect temporal intervals corresponding to a language description from untrimmed videos with only video-level video-language correspondence.<n>For an anchor sample, most existing approaches generate negative samples either from other videos or within the same video for contrastive learning.<n>We propose Positive Sample Mining (PSM), a novel framework that mines positive samples from the training set to provide more discriminative supervision.
• Score: 64.25904019001449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of weakly supervised temporal sentence grounding (WSTSG) aims to detect temporal intervals corresponding to a language description from untrimmed videos with only video-level video-language correspondence. For an anchor sample, most existing approaches generate negative samples either from other videos or within the same video for contrastive learning. However, some training samples are highly similar to the anchor sample, directly regarding them as negative samples leads to difficulties for optimization and ignores the correlations between these similar samples and the anchor sample. To address this, we propose Positive Sample Mining (PSM), a novel framework that mines positive samples from the training set to provide more discriminative supervision. Specifically, for a given anchor sample, we partition the remaining training set into semantically similar and dissimilar subsets based on the similarity of their text queries. To effectively leverage these correlations, we introduce a PSM-guided contrastive loss to ensure that the anchor proposal is closer to similar samples and further from dissimilar ones. Additionally, we design a PSM-guided rank loss to ensure that similar samples are closer to the anchor proposal than to the negative intra-video proposal, aiming to distinguish the anchor proposal and the negative intra-video proposal. Experiments on the WSTSG and grounded VideoQA tasks demonstrate the effectiveness and superiority of our method.

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