AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning

2026-07-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition, Natural Language Processing · Depth: Expert, quick

Summary

AVSCap is a novel framework for omni-modal video captioning designed to overcome the limitations of existing large multimodal models in describing co-evolving audio and visual events. It focuses on explicit cross-modal event binding, moving beyond simple audio transcription and visual captioning. The framework introduces AVSCap-130K, a 130,000-entry tri-modal training corpus created via a decoupled-then-fused pipeline. It also presents AVSCap-7B, a 7-billion parameter captioner trained using a two-stage strategy combining supervised fine-tuning with sample-efficient reinforcement learning, where RL showed greater gains than increased SFT data. To evaluate performance, AVSCapBench was developed, a benchmark that assesses visual, audio, and synergy events with fine-grained recall. Experiments demonstrate AVSCap-7B's superior performance among open-source models, particularly in non-speech audio coverage and cross-modal binding.

Key takeaway

For Machine Learning Engineers developing multimodal video captioning systems, AVSCap demonstrates that explicit cross-modal event binding is essential for capturing audio-visual synergy. You should prioritize training strategies that incorporate reinforcement learning with hybrid rewards, as this approach yields greater performance gains than simply increasing supervised fine-tuning data. Consider developing fine-grained benchmarks to accurately evaluate non-speech audio coverage and cross-modal relationships in your models.

Key insights

Omni-modal video captioning requires explicit cross-modal event binding to describe co-evolving audio-visual synergy.

Principles

• Cross-modal event binding is crucial for omni-modal captioning.
• Reinforcement learning can yield greater gains than more SFT data.
• Decoupled-then-fused pipeline improves tri-modal corpus construction.

Method

AVSCap constructs a tri-modal corpus via a decoupled-then-fused pipeline, then trains a 7B captioner using supervised fine-tuning followed by reinforcement learning with hybrid rewards for acoustic completeness and audio-visual synergy.

In practice

• Develop benchmarks evaluating fine-grained event recall.
• Prioritize RL over SFT data scaling for performance gains.
• Anchor visual and acoustic evidence before composing captions.

Topics

• Omni-modal Video Captioning
• Cross-modal Event Binding
• Reinforcement Learning
• Multimodal Models
• AVSCapBench

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.