video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models
This work addresses the understudied aspect of speech understanding in video processing for applications in AI and multimedia, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of speech understanding in video processing by proposing video-SALMONN, an end-to-end audio-visual large language model that integrates speech, audio, and visual elements, achieving over 25% absolute accuracy improvements on video-QA tasks and over 30% on audio-visual QA tasks with human speech.
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 \texttt{\url{https://github.com/bytedance/SALMONN/}}.