Jingyun Hua — accepted AI-conference papers

2026

AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration

Xinlong Chen, Yue Ding, Weihong Lin, Jingyun Hua, Linli Yao, Yang Shi +5

ICLR 2026poster

Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding and generation. In this paper, we present **AVoCaDO**, a powerful audiovisual video captioner driven by the temporal or…

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2025

HAIC: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models

Xiao Wang, Jingyun Hua, Weihong Lin, Yuanxing Zhang, Fuzheng Zhang, Jianlong Wu +2

ACL 2025long

Recent Multi-modal Large Language Models (MLLMs) have made great progress in video understanding. However, their performance on videos involving human actions is still limited by the lack of high-quality data. To address this, we introduce a two-stage data annotation pipeline. First, we design strat…

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