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20242026
most citedvideo-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model

2 citations · 6 across the 18 of their papers we have counts for

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8 papers · 1 filter

eess.AS2025

Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking

Siyin Wang, Zengrui Jin, Changli Tang +26

In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities…

cs.SD2025

Speech-Audio Compositional Attacks on Multimodal LLMs and Their Mitigation with SALMONN-Guard

Yudong Yang, Xuezhen Zhang, Zhifeng Han +6

Recent progress in LLMs has enabled understanding of audio signals, but has also exposed new safety risks arising from complex audio inputs that are inadequately handled by current…

cs.SD2025

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model

Yudong Yang, Xiaokang Liu, Shaofeng zhao +3

Speech therapy is essential for rehabilitating speech disorders caused by neurological impairments such as stroke. However, traditional manual and computer-assisted systems are lim…

cs.CV2025

video-SALMONN S: Memory-Enhanced Streaming Audio-Visual LLM

Guangzhi Sun, Yixuan Li, Xiaodong Wu +4

Long-duration streaming video understanding is fundamental for future AI agents, yet remains limited by ineffective long-term memory. We introduce video-SALMONN S, a memory-enhance…

cs.CV2025★ 1 cited

video-SALMONN 2: Caption-Enhanced Audio-Visual Large Language Models

Changli Tang, Yixuan Li, Yudong Yang +5

We present video-SALMONN 2, a family of audio-visual large language models that set new state-of-the-art (SOTA) results in video description and question answering (QA). Our core c…

cs.CV2025

Audio-centric Video Understanding Benchmark without Text Shortcut

Yudong Yang, Jimin Zhuang, Guangzhi Sun +7

Audio often serves as an auxiliary modality in video understanding tasks of audio-visual large language models (LLMs), merely assisting in the comprehension of visual information.…

cs.CV2025

Improving LLM Video Understanding with 16 Frames Per Second

Yixuan Li, Changli Tang, Jimin Zhuang +5

Human vision is dynamic and continuous. However, in video understanding with multimodal large language models (LLMs), existing methods primarily rely on static features extracted f…

cs.CV2025★ 2 cited

video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model

Guangzhi Sun, Yudong Yang, Jimin Zhuang +5

While recent advancements in reasoning optimization have significantly enhanced the capabilities of large language models (LLMs), existing efforts to improve reasoning have been li…