{"ID":3083653,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:32:54.120957816Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06294","arxiv_id":"2606.06294","title":"Towards One-to-Many Temporal Grounding","abstract":"Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal Grounding (OMTG). Previous state-of-the-art MLLMs, optimized for one-to-one settings, struggle in this context, often yielding near-zero scores due to a lack of event cardinality perception. To bridge this gap, we present a systematic solution with three key contributions. First, we establish the first comprehensive OMTG benchmark, introducing Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1) as evaluation metrics. Second, we curate a high-quality OMTG dataset comprising 56k samples through a sophisticated construction pipeline. Third, we develop novel temporal and caption reward functions specifically designed for OMTG. In particular, the caption reward leverages Chain-of-Thought reasoning over dense video captions to explicitly guide policy optimization toward both preciseness and completeness. Extensive experiments show our model achieves a new state-of-the-art EtF1 of 43.65\\% on OMTG Bench, outperforming Gemini 2.5 Pro and Seed-1.8 by 15.85\\% and 15.61\\%, respectively.","short_abstract":"Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal Grounding (OMTG). Pr...","url_abs":"https://arxiv.org/abs/2606.06294","url_pdf":"https://arxiv.org/pdf/2606.06294v1","authors":"[\"Qi Xu\",\"Yue Tan\",\"Shihao Chen\",\"Jiahao Meng\",\"Anna Wang\",\"Shunping Ji\",\"Hao Fei\",\"Jason Li\"]","published":"2026-06-04T15:31:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}