Towards One-to-Many Temporal Grounding
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 (E...
| Authors | Qi Xu, Yue Tan, Shihao Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 June 2026 |
| Last updated at source | 21 June 2026 |
Models and datasets referencing this paper
- insomnia7/omtg56kDataset
- insomnia7/omtg_benchDataset
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