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Number it: Temporal Grounding Videos like Flipping Manga

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arxiv 2411.10332 v3 pith:3TN6ZRNZ submitted 2024-11-15 cs.CV

Yongliang Wu , Xinting Hu , Yuyang Sun , Yizhou Zhou , Wenbo Zhu , Fengyun Rao , Bernt Schiele , Xu Yang This is my paper
classification cs.CV
keywords temporalvideonumprovid-llmsgroundingvisualcontentflipping
verification ladder T0 review T1 audit T2 compute T3 formal
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Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue. However, they struggle to extend this visual understanding to tasks requiring precise temporal localization, known as Video Temporal Grounding (VTG). To address this gap, we introduce Number-Prompt (NumPro), a novel method that empowers Vid-LLMs to bridge visual comprehension with temporal grounding by adding unique numerical identifiers to each video frame. Treating a video as a sequence of numbered frame images, NumPro transforms VTG into an intuitive process: flipping through manga panels in sequence. This allows Vid-LLMs to "read" event timelines, accurately linking visual content with corresponding temporal information. Our experiments demonstrate that NumPro significantly boosts VTG performance of top-tier Vid-LLMs without additional computational cost. Furthermore, fine-tuning on a NumPro-enhanced dataset defines a new state-of-the-art for VTG, surpassing previous top-performing methods by up to 6.9\% in mIoU for moment retrieval and 8.5\% in mAP for highlight detection. The code will be available at https://github.com/yongliang-wu/NumPro.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TimePLE: Rethinking Temporal Representation for Video Temporal Grounding

    cs.CV 2026-07 conditional novelty 6.0 of 10

    TimePLE predicts a whole video interval as a joint distribution over a position-duration square, rather than predicting start and end separately, and reports higher mIoU across four VTG benchmarks.

  2. ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning

    cs.CV 2025-05 conditional novelty 6.0 of 10

    ViaRL uses rule-based reinforcement learning to train a frame selector for video QA, improving Qwen2.5-VL on VideoMME, LVBench, and MLVU by several points.

  3. VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism

    cs.CV 2025-06 conditional novelty 5.0 of 10

    VReST combines Monte Carlo tree search with a self-reward signal inside a vision-language model to get higher accuracy than CoT, ToT, or voting baselines on MathVista, MathVision, and CharXiv, while spending several t...

  4. RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought

    cs.CV 2025-06 conditional novelty 5.0 of 10

    RSVP couples region-grid visual prompting and multimodal chain-of-thought reasoning with a BEiT-3/SAM segmentation module, achieving state-of-the-art zero-shot results on ReasonSeg and SegInW.

  5. A Survey on Video Temporal Grounding with Multimodal Large Language Model

    cs.CV 2025-08 unverdicted novelty 3.0 of 10

    A taxonomized review of video temporal grounding with multimodal large language models, covering model roles, training paradigms, feature processing, benchmarks, and open problems.

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