Question-Aware Gaussian Experts for Audio-Visual Question Answering
Abstract
Audio-Visual Question Answering (AVQA) requires not only question-based multimodal reasoning but also precise temporal grounding to capture subtle dynamics for accurate prediction. However, existing methods mainly use question information implicitly, limiting focus on question-specific details. Furthermore, most studies rely on uniform frame sampling, which can miss key question-relevant frames. Although recent Top-K frame selection methods aim to address this, their discrete nature still overlooks fine-grained temporal details. This paper proposes QA-TIGER, a novel framework that explicitly incorporates question information and models continuous temporal dynamics. Our key idea is to use Gaussian-based modeling to adaptively focus on both consecutive and non-consecutive frames based on the question, while explicitly injecting question information and applying progressive refinement. We leverage a Mixture of Experts (MoE) to flexibly implement multiple Gaussian models, activating temporal experts specifically tailored to the question. Extensive experiments on multiple AVQA benchmarks show that QA-TIGER consistently achieves state-of-the-art performance. Code is available at https://aim-skku.github.io/QA-TIGER/
Cite
@article{arxiv.2503.04459,
title = {Question-Aware Gaussian Experts for Audio-Visual Question Answering},
author = {Hongyeob Kim and Inyoung Jung and Dayoon Suh and Youjia Zhang and Sangmin Lee and Sungeun Hong},
journal= {arXiv preprint arXiv:2503.04459},
year = {2025}
}
Comments
CVPR 2025. Code is available at https://github.com/AIM-SKKU/QA-TIGER