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Published on: May 7, 2019
Re-thinking Temporal Search for Long-Form Video Understanding
Jinhui Ye1, Zihan Wang2, Haosen Sun2
1Stanford University.
Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|August 6, 2026
Summary
This study introduces a new method for finding key moments in long videos, improving how AI understands them. The new framework, T*, significantly boosts the performance of advanced AI models in video analysis.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Understanding long-form videos is a major challenge in computer vision.
- Current state-of-the-art (SOTA) long-context vision-language models (VLMs) face limitations in temporal search efficiency.
- Existing temporal search methods show a significant research gap, with low performance on benchmarks like LongVideoBench.
Purpose of the Study:
- To address the challenge of efficient temporal search in long-form video understanding.
- To introduce a novel dataset (LV-Haystack) and a lightweight temporal search framework (T*) for improved video analysis.
- To enhance the performance of SOTA VLMs in understanding long videos through efficient frame retrieval.
Main Methods:
- Framing temporal search as a 'Long Video Haystack' problem, focusing on retrieving a minimal set of relevant frames.
- Introducing LV-Haystack, a large-scale dataset with 480 hours of video and 15,092 annotated instances for training and evaluation.
- Proposing T*, a lightweight temporal search framework that reframes temporal search as spatial search using visual localization techniques and an adaptive zooming-in mechanism.
Main Results:
- LV-Haystack dataset reveals significant limitations in current SOTA temporal search methods, achieving only a 2.1% temporal F1 score on LongVideoBench.
- The T* framework significantly improves SOTA long-form video understanding when integrated with existing models.
- T* enhances GPT-4o's performance to 53.1% and LLaVA-OneVision-OV-72B's to 62.4% on the LongVideoBench XL subset within a 32-frame budget.
Conclusions:
- The 'Long Video Haystack' formulation and LV-Haystack dataset provide a new direction for improving temporal search in videos.
- The T* framework offers an efficient and effective solution for enhancing long-form video understanding by leveraging spatial search principles.
- This work demonstrates substantial performance gains in advanced VLMs, paving the way for more capable AI in video analysis.
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