What is it about?

Answering a question about a long video means finding the right evidence. A question about an object's colour depends on what appears on screen, while a question about why something happened may require information from subtitles. Combining these clues in the same way for every question can introduce distractions and lead to incorrect answers. We developed Q-Gate, a method that adjusts the importance of different clues according to the question. It brings together information about specific objects, the overall meaning of a scene, and the dialogue or narrative conveyed by subtitles. It then selects a small set of relevant video frames and presents them with time-aligned subtitles to an existing AI model. Experiments on video question-answering benchmarks showed improved accuracy in most evaluated settings, without additional model training.

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Why is it important?

Giving an AI system more information does not always help it answer correctly. Irrelevant subtitles can distract it from visual details, while relying on pictures alone can miss important explanations in the dialogue. Q-Gate makes the balance between these clues depend on the question. It can work with existing AI models without additional training, making it a practical approach when only a limited number of video frames can be processed. Its explicit weighting of different clues also makes it easier to inspect what information the system prioritizes. This work highlights the value of managing information carefully when building systems that combine visual and textual evidence.

Perspectives

For me, the main message of this work is that the value of information depends on the question being asked. The same video can require very different evidence for different questions, and useful clues for one task can become distractions for another. With Q-Gate, we explored how to make this balance adaptive and easier to understand. I hope this work encourages further research into how AI systems decide which information deserves attention, and how much weight to give it.

Shaoguang Wang
Hong Kong University of Science and Technology

Read the Original

This page is a summary of: Where to Focus: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding, November 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3767308.3835040.
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