7 citations · 17 across the 21 of their papers we have counts for
8 papers · 1 filter
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…
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…
SALMONN-omni: A Standalone Speech LLM without Codec Injection for Full-duplex Conversation
Wenyi Yu, Siyin Wang, Xiaoyu Yang +7
In order to enable fluid and natural human-machine speech interaction, existing full-duplex conversational systems often adopt modular architectures with auxiliary components such…
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions
Siyin Wang, Wenyi Yu, Xianzhao Chen +7
This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical…
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…