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.

本文提出了一种名为 VideoLLM 的新框架，它利用了自然语言处理（NLP）预训练 LLMs 的序列推理能力来进行视频序列理解。通过精心设计的模态编码器和语义转换器，将不同来源的输入转换为统一的标记序列，然后将其馈入仅解码的 LLM 中。在实验中，作者评估了 VideoLLM 在多个任务上的表现，证明了 LLMs 的理解和推理能力可以有效地转移到视频理解任务中。