MM-VID: Advancing Video Understanding with GPT-4V(ision)
This addresses the problem of understanding long-form and complex videos for applications in media analysis and interactive environments, representing an incremental advancement by integrating existing tools.
The authors tackled video understanding by developing MM-VID, a system that uses GPT-4V and specialized tools to transcribe multimodal video elements into scripts, enabling advanced capabilities like audio description and character identification; experimental results show effectiveness across various video genres and lengths.
We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video understanding. MM-VID is designed to address the challenges posed by long-form videos and intricate tasks such as reasoning within hour-long content and grasping storylines spanning multiple episodes. MM-VID uses a video-to-script generation with GPT-4V to transcribe multimodal elements into a long textual script. The generated script details character movements, actions, expressions, and dialogues, paving the way for large language models (LLMs) to achieve video understanding. This enables advanced capabilities, including audio description, character identification, and multimodal high-level comprehension. Experimental results demonstrate the effectiveness of MM-VID in handling distinct video genres with various video lengths. Additionally, we showcase its potential when applied to interactive environments, such as video games and graphic user interfaces.