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arXiv · 2608.06930

AVCap: Reinforcing Audio-Video Joint Caption with Detail-Aware Reward

Mingyang Wu·Kaituo Feng·Bohao Li·Kaixiong Gong·Zihao Yin·Xiangyu Yue
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Abstract

Detailed audio-video joint captioning is essential for multimodal video understanding and generation. However, prior works are constrained by three main limitations: (1) the scarcity of high-quality public datasets with fine-grained audio-visual joint captions; (2) reinforcement-learning methods that rely on coarse reward signals; and (3) the lack of a benchmark and metric for evaluating detailed audiovisual captions at the atomic level. To address these challenges, we propose: (1) AVCap-100K, a high-quality dataset of 100K temporally aligned, detail-rich audio-video captions; (2) AVCap, a model optimized via Detail-Aware GRPO (Da-GRPO) that achieves state-of-the-art performance among open-source models and matches or surpasses proprietary models on several evaluations; and (3) AVCap-Bench and AVCap-Score, a specialized benchmark and metric for evaluating atomic-level details in audiovisual captions. Our code, models, and datasets are available at https://huggingface.co/collections/Apryle/avcap.

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Mingyang Wu, Kaituo Feng, Bohao Li, Kaixiong Gong, Zihao Yin, Xiangyu Yue. 2026-08-07. AVCap: Reinforcing Audio-Video Joint Caption with Detail-Aware Reward. https://arxiv.org/abs/2608.06930

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