Needle in a Haystack: Finding Exact Moments in Long Videos

Long videos are everywhere, with footage of movies, YouTube videos, body-cam recordings, and AR/VR often running for tens of minutes or even hours. Now imagine asking a simple question like “Where are the scissors in this video?”

Finding the exact moment in a one-hour video is like searching for a needle in a haystack. While many existing models work well for short clips of a few seconds, they struggle badly when videos become long.

This is the problem addressed in the paper “RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos”. In this work, MCML Junior Member Tanveer Hannan, along with MCML Director Thomas Seidl and collaborators Md Mohaiminul Islam and Gedas Bertasius propose a new model that can efficiently and accurately locate precise moments inside very long videos, all in a single, unified system called RGNet.

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The Idea Behind the Paper

Most long-video search systems work in two steps. First, they retrieve a short clip that might contain the event described by the text query. Then, they try to precisely locate the start and end of the event within that clip. The main problem with this approach is that if the first step retrieves the wrong clip, the second step cannot recover.

RGNet addresses this issue by combining clip retrieval and moment localization into a single model. Instead of treating them as separate tasks, RGNet learns both at the same time. The model looks at videos at two levels: longer clips for overall context, and individual frames for fine-grained details. By directly optimizing, RGNet learns to focus on the most relevant parts of the video and better capture short events in very long videos.

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The Intuition Behind RGNet

RGNet splits a long video into overlapping clips and processes them together with the text query using a transformer. Rather than analyzing every frame equally, the model learns to ignore irrelevant content and focus on frames that are likely related to the query. Based on this focused representation, RGNet identifies the most relevant clip and predicts the exact start and end time of the event.

A key advantage of this design is that clip retrieval is trained using the same objective as final localization. This encourages the model to retrieve clips that support precise grounding, rather than clips that are only loosely related to the text.

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Results

The authors evaluate RGNet on two challenging benchmarks for long-video understanding: Ego4D-NLQ, and MAD (movie-length videos). Across both datasets, RGNet outperforms previous methods, achieving higher accuracy in both clip retrieval and moment localization. In addition, RGNet is more efficient, as it requires fewer clips to make accurate predictions.

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Why This Matters

In real-world videos, important events are often short and easy to miss. Systems that separate clip retrieval from localization are fragile and can fail when the wrong clip is selected. By unifying these steps, RGNet provides a more robust and efficient approach to long-video understanding. This makes it well-suited for applications such as video search, video assistants, and large-scale video analysis.

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Challenges and Next Steps

While RGNet shows strong results, challenges remain. Training is still computationally expensive, and the model relies on pre-extracted visual features. Scaling to even longer videos or learning directly from raw video remains an open direction for future work.

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Further Reading & Reference

If you’d like to learn more about how RGNet enables precise moment localization in long videos, the full paper and code are available below. The work was presented at ECCV 2024, one of the leading international conferences in computer vision.

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