Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video
  Understanding
Authors: name: Hang Zhang, name: Xin Li, name: Lidong Bing

We present Video-LLaMA, a multi-modal framework that empowers Large Language
Models (LLMs) with the capability of understanding both visual and auditory
content in the video. Video-LLaMA bootstraps cross-modal training from the
frozen pre-trained visual \& audio encoders and the frozen LLMs. Unlike
previous vision- LLMs that focus on static image comprehensions such as
MiniGPT-4~zhu2023minigpt and LLaVA~liu2023visualit, Video-LLaMA
tackles two challenges in video understanding: (1) capturing the temporal
changes in visual scenes, (2) integrating audio-visual signals. For the first
challenge, we propose Video Q-former to extend the pre-trained image encoder to
a video encoder and introduce a video-to-text generation task to learn
video-language correspondence. For the second challenge, we leverage
ImageBind~girdhar2023imagebind as the pre-trained audio encoder which
performs exceptionally well in aligning different modalities to a common
embedding space. And then introduce an Audio Q-former to learn auditory query
tokens. To align the output of both visual \& audio encoder with LLM's
embedding space, we train Video-LLaMA on a large-scale vision caption dataset
and a hign-quantity vision-instruction-tuning dataset. We found Video-LLaMA
showcases the ability to perceive and comprehend video content, generating
meaningful responses that are grounded in the visual and auditory information
present in the videos. This highlights the potential of Video-LLaMA as a
promising prototype for audio-visual AI assistants. Our code, pre-trained
model, and demo are available at
https://github.com/DAMO-NLP-SG/Video-LLaMA.