@inproceedings{wang-etal-2026-avscap,
title = "{AVSC}ap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning",
author = "Wang, Yanghai and
Wang, Jiahao and
Tang, Jiafu and
Zhang, Yuanxing and
Cao, Zhe and
Bian, Hanyan and
Zhang, Zijie and
Luo, Weiliang and
Pan, Zhiyu and
Dong, Zixuan and
Liu, Jiaheng and
Zhang, Zhaoxiang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1433/",
pages = "32731--32761",
ISBN = "979-8-89176-459-0",
abstract = "Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects co-evolve over time. Existing large multimodal models often fail at this relational step, treating audio and visual streams as loosely coupled observations, relying heavily on automatic speech recognition, and under-specifying non-speech sounds and their links to visual events. We present AVSCap, a framework for audio-visual captioning centered on explicit cross-modal event binding. First, we construct AVSCap-130K, a tri-modal training corpus generated by a decoupled-then-fused pipeline that anchors visual and acoustic evidence independently before composing temporally grounded omni-modal captions. Second, we train AVSCap-7B, a 7B captioner with a two-stage strategy: supervised fine-tuning establishes baseline captioning ability, while a sample-efficient reinforcement learning stage uses hybrid rewards to optimize acoustic completeness and audio-visual synergy. Our scaling analysis shows that reinforcement learning brings larger gains than simply increasing SFT data. Third, we introduce AVSCapBench, a human-curated benchmark that decomposes captions into visual, audio, and synergy events and evaluates them with fine-grained event recall. Experiments on AVSCapBench and external benchmarks show that AVSCap-7B improves non-speech audio coverage and cross-modal binding, delivering the best overall performance among evaluated open-source models."
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<abstract>Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects co-evolve over time. Existing large multimodal models often fail at this relational step, treating audio and visual streams as loosely coupled observations, relying heavily on automatic speech recognition, and under-specifying non-speech sounds and their links to visual events. We present AVSCap, a framework for audio-visual captioning centered on explicit cross-modal event binding. First, we construct AVSCap-130K, a tri-modal training corpus generated by a decoupled-then-fused pipeline that anchors visual and acoustic evidence independently before composing temporally grounded omni-modal captions. Second, we train AVSCap-7B, a 7B captioner with a two-stage strategy: supervised fine-tuning establishes baseline captioning ability, while a sample-efficient reinforcement learning stage uses hybrid rewards to optimize acoustic completeness and audio-visual synergy. Our scaling analysis shows that reinforcement learning brings larger gains than simply increasing SFT data. Third, we introduce AVSCapBench, a human-curated benchmark that decomposes captions into visual, audio, and synergy events and evaluates them with fine-grained event recall. Experiments on AVSCapBench and external benchmarks show that AVSCap-7B improves non-speech audio coverage and cross-modal binding, delivering the best overall performance among evaluated open-source models.</abstract>
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%0 Conference Proceedings
%T AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning
%A Wang, Yanghai
%A Wang, Jiahao
%A Tang, Jiafu
%A Zhang, Yuanxing
%A Cao, Zhe
%A Bian, Hanyan
%A Zhang, Zijie
%A Luo, Weiliang
%A Pan, Zhiyu
%A Dong, Zixuan
%A Liu, Jiaheng
%A Zhang, Zhaoxiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-avscap
%X Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects co-evolve over time. Existing large multimodal models often fail at this relational step, treating audio and visual streams as loosely coupled observations, relying heavily on automatic speech recognition, and under-specifying non-speech sounds and their links to visual events. We present AVSCap, a framework for audio-visual captioning centered on explicit cross-modal event binding. First, we construct AVSCap-130K, a tri-modal training corpus generated by a decoupled-then-fused pipeline that anchors visual and acoustic evidence independently before composing temporally grounded omni-modal captions. Second, we train AVSCap-7B, a 7B captioner with a two-stage strategy: supervised fine-tuning establishes baseline captioning ability, while a sample-efficient reinforcement learning stage uses hybrid rewards to optimize acoustic completeness and audio-visual synergy. Our scaling analysis shows that reinforcement learning brings larger gains than simply increasing SFT data. Third, we introduce AVSCapBench, a human-curated benchmark that decomposes captions into visual, audio, and synergy events and evaluates them with fine-grained event recall. Experiments on AVSCapBench and external benchmarks show that AVSCap-7B improves non-speech audio coverage and cross-modal binding, delivering the best overall performance among evaluated open-source models.
%U https://aclanthology.org/2026.emnlp-main.1433/
%P 32731-32761
Markdown (Informal)
[AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning](https://aclanthology.org/2026.emnlp-main.1433/) (Wang et al., EMNLP 2026)
ACL
- Yanghai Wang, Jiahao Wang, Jiafu Tang, Yuanxing Zhang, Zhe Cao, Hanyan Bian, Zijie Zhang, Weiliang Luo, Zhiyu Pan, Zixuan Dong, Jiaheng Liu, and Zhaoxiang Zhang. 2026. AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32731–32761, Budapest, Hungary. Association for Computational Linguistics.