20 citations · 33 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
WAVE: Learning Unified & Versatile Audio-Visual Embeddings with Multimodal LLM
Changli Tang, Qinfan Xiao, Ke Mei +3
While embeddings from multimodal large language models (LLMs) excel as general-purpose representations, their application to dynamic modalities like audio and video remains underex…
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…
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.…
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…
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…