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arXiv · 2602.05847

OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention

Zhangquan Chen·Jiale Tao·Ruihuang Li·Yihao Hu·Ruitao Chen·Zhantao Yang·Xinlei Yu·Haodong Jing·Manyuan Zhang·Shuai Shao·Biao Wang·Qinglin Lu·Ruqi Huang
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Abstract

While humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings, existing omnivideo models still face substantial challenges on audio-visual understanding tasks. In this paper, we propose OmniVideo-R1, a novel reinforced framework that improves mixed-modality reasoning. OmniVideo-R1 empowers models to "think with omnimodal cues" by two key strategies: (1) query-intensive grounding based on self-supervised learning paradigms; and (2) modality-attentive fusion built upon contrastive learning paradigms. Extensive experiments on multiple benchmarks demonstrate that OmniVideo-R1 consistently outperforms strong baselines, highlighting its effectiveness and robust generalization capabilities.

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Zhangquan Chen, Jiale Tao, Ruihuang Li, Yihao Hu, Ruitao Chen, Zhantao Yang, Xinlei Yu, Haodong Jing, Manyuan Zhang, Shuai Shao, Biao Wang, Qinglin Lu, Ruqi Huang. 2026-02-05. OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention. https://arxiv.org/abs/2602.05847

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