Papers › R²-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
R²-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
Ye Liu, Jixuan He, Wanhua Li, Junsik Kim, Donglai Wei, Hanspeter Pfister, Chang Wen Chen
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VTG models are built upon frame-wise final-layer CLIP features, aided by additional temporal backbones (e.g., SlowFast) with sophisticated temporal reasoning mechanisms. In this work, we claim that CLIP itself already shows great potential for fine-grained spatial-temporal modeling, as each layer offers distinct yet useful information under different granularity levels. Motivated by this, we propose Reversed Recurrent Tuning (R²-Tuning), a parameter- and memory-efficient transfer learning framework for video temporal grounding. Our method learns a lightweight R² Block containing only 1.5% of the total parameters to perform progressive spatial-temporal modeling. Starting from the last layer of CLIP, R² Block recurrently aggregates spatial features from earlier layers, then refines temporal correlation conditioning on the given query, resulting in a coarse-to-fine scheme. R²-Tuning achieves state-of-the-art performance across three VTG tasks (i.e., moment retrieval, highlight detection, and video summarization) on six public benchmarks (i.e., QVHighlights, Charades-STA, Ego4D-NLQ, TACoS, YouTube Highlights, and TVSum) even without the additional backbone, demonstrating the significance and effectiveness of the proposed scheme. Our code is available at https://github.com/yeliudev/R2-Tuning.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Highlight Detection | QVHighlights | R^2-Tuning | Hit@1 | 64.20 | #6 of 21 | Archive leaderboard | report |
| Highlight Detection | QVHighlights | R^2-Tuning | mAP | 40.75 | #6 of 21 | Archive leaderboard | report |
| Moment Retrieval | QVHighlights | R^2-Tuning | R@1 IoU=0.5 | 68.03 | #12 of 32 | Archive leaderboard | report |
| Moment Retrieval | QVHighlights | R^2-Tuning | R@1 IoU=0.7 | 49.35 | #12 of 32 | Archive leaderboard | report |
| Moment Retrieval | QVHighlights | R^2-Tuning | mAP | 46.17 | #12 of 32 | Archive leaderboard | report |
| Moment Retrieval | QVHighlights | R^2-Tuning | mAP@0.5 | 69.04 | #12 of 32 | Archive leaderboard | report |
| Moment Retrieval | QVHighlights | R^2-Tuning | mAP@0.75 | 47.56 | #12 of 32 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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