{"ID":5937948,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T11:02:02.505471457Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03825","arxiv_id":"2607.03825","title":"Q-TriM: Question-Guided Tri-Modal Attention for Audio-Visual Question Answering","abstract":"Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio. However, many AVQA systems rely on deeply stacked layers of self- and cross attention across text, video, and audio. Such sequential stacking may incur loss of information such as subtle inter-modal cues over the layers, causing errors to accumulate across sequential attention layers during the fusion. We introduce Q-TriM which performs multi-modal fusion in a shallow and parallel manner instead of a deep and sequential manner. For Q-TriM, we propose a novel framework for attention operation incorporating video and audio conditioned on text. As a result, we obtain not only standard cross attention outputs but also Tri-Modal Attention representations in which Query, Key, and Value come from distinct modalities. These attention representations are combined in parallel at a single stage, thus avoiding the multi-modal fusion with deep stacks in order to mitigate error accumulation and depth-induced issues. Q-TriM achieves state-of-the-art performance on three AVQA benchmarks, including substantial gains on MUSIC-AVQA-R, which demonstrates its robustness and out-of-distribution generalization. Code is available at https://github.com/Sunghun95/Q-TriM","short_abstract":"Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio. However, many AVQA systems rely on deeply stacked layers of self- and cross attention across text, video, and audio. Such sequential stacking may incur loss of information such as subtle inter-mo...","url_abs":"https://arxiv.org/abs/2607.03825","url_pdf":"https://arxiv.org/pdf/2607.03825v1","authors":"[\"SungHun Kim\",\"SeungJun Baek\"]","published":"2026-07-04T11:21:33Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":614000,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T03:14:33.014478982Z","DeletedAt":null,"paper_id":5937948,"paper_url":"https://arxiv.org/abs/2607.03825","paper_title":"Q-TriM: Question-Guided Tri-Modal Attention for Audio-Visual Question Answering","repo_url":"https://github.com/Sunghun95/Q-TriM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}