EVA-Cap aligns audiovisual captions to events, lifting ChronusAV by 9%
EVA-Cap is a new framework for audiovisual video captioning that decomposes captions into atomic audiovisual events, replacing unstructured text generation with event-centric alignment. The authors train it with supervised fine-tuning and GRPO on a self-curated dataset, EVA-Data, optimizing three event-centric targets: global event completeness, intra-event attribution and inter-event synchronization. The paper reports a 9.0% average gain on ChronusAV event-centric evaluation and a 3.3% gain on the general WorldSense benchmark. It was accepted at NeurIPS 2026, and the code is said to be released at the link given in the paper.
It reframes audiovisual caption quality around decomposable event alignment rather than prose style, offering a measurable way to train and evaluate multimodal models on long audio-video content.