video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models
Authors: name: Guangzhi Sun, name: Wenyi Yu, name: Changli Tang, name: Xianzhao Chen, name: Tian Tan, name: Wei Li, name: Lu Lu, name: Zejun Ma, name: Yuxuan Wang, name: Chao Zhang

Speech understanding as an element of the more generic video understanding
using audio-visual large language models (av-LLMs) is a crucial yet
understudied aspect. This paper proposes video-SALMONN, a single end-to-end
av-LLM for video processing, which can understand not only visual frame
sequences, audio events and music, but speech as well. To obtain fine-grained
temporal information required by speech understanding, while keeping efficient
for other video elements, this paper proposes a novel multi-resolution causal
Q-Former (MRC Q-Former) structure to connect pre-trained audio-visual encoders
and the backbone large language model. Moreover, dedicated training approaches
including the diversity loss and the unpaired audio-visual mixed training
scheme are proposed to avoid frames or modality dominance. On the introduced
speech-audio-visual evaluation benchmark, video-SALMONN achieves more than 25\%
absolute accuracy improvements on the video-QA task and over 30\% absolute
accuracy improvements on audio-visual QA tasks with human speech. In addition,
video-SALMONN demonstrates remarkable video comprehension and reasoning
abilities on tasks that are unprecedented by other av-LLMs. Our training code
and model checkpoints are available at
\url{https://github.com/bytedance/SALMONN/}.