# Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering

Source: [Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering](<https://www.sota2.com/research/paper/69b8e05c0636e5bf4afbefa5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-18T05:00:11.457Z

Authors: Minchan Kwon, Hyounguk Shon, Junmo Kim

Published: 2026-03-16T08:07:14.000Z

## Abstract

Large multimodal models (LMMs) have recently demonstrated remarkable performance in video question answering (VideoQA), yet reasoning over video remains challenging due to high inference cost and diluted information. Keyframe selection offers efficiency and sharper reasoning but suffers from sparse supervision and redundant frame choices when relying only on image-text similarity. We present a question-aware keyframe selection framework with two components: pseudo keyframe labels derived from LMMs that provide informative supervision and a coverage regularization that promotes diverse, complementary evidence across time. Experiments on NExT-QA show that our method significantly improves accuracy, especially for temporal and causal question types, establishing keyframe selection as an effective and learnable module for VideoQA.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.14953>)

[PDF](<https://arxiv.org/pdf/2603.14953.pdf>)