  With the exponential growth of video data, there is an urgent need for
automated technology to analyze and comprehend video content. However, existing
video understanding models are often task-specific and lack a comprehensive
capability of handling diverse tasks. The success of large language models
(LLMs) like GPT has demonstrated their impressive abilities in sequence causal
reasoning. Building upon this insight, we propose a novel framework called
VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs
from natural language processing (NLP) for video sequence understanding.
VideoLLM incorporates a carefully designed Modality Encoder and Semantic
Translator, which convert inputs from various modalities into a unified token
sequence. This token sequence is then fed into a decoder-only LLM.
Subsequently, with the aid of a simple task head, our VideoLLM yields an
effective unified framework for different kinds of video understanding tasks.
To evaluate the efficacy of VideoLLM, we conduct extensive experiments using
multiple LLMs and fine-tuning methods. We evaluate our VideoLLM on eight tasks
sourced from four different datasets. The experimental results demonstrate that
the understanding and reasoning capabilities of LLMs can be effectively
transferred to video understanding tasks. We release the code at
https://github.com/cg1177/VideoLLM.
