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cs.CV2026
AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning
Yanghai Wang, Jiahao Wang, Jiafu Tang +9
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…
cs.CV2026
Through the Lens of Contrast: Self-Improving Visual Reasoning in VLMs
Zhiyu Pan, Yizheng Wu, Jiashen Hua +5
Reasoning has emerged as a key capability of large language models. In linguistic tasks, this capability can be enhanced by self-improving techniques that refine reasoning paths fo…
cs.CV2025
MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues
Yaning Pan, Qianqian Xie, Guohui Zhang +13
The recent development of Multimodal Large Language Models (MLLMs) has significantly advanced AI's ability to understand visual modalities. However, existing evaluation benchmarks…