MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Understand

This paper introduces MiniGPT4-Video, a multimodal Large Language Model (LLM) designed specifically for video understanding.

• The model is capable of processing both temporal visual and textual data, making it adept at understanding the complexities of videos.
• Building upon the success of MiniGPT-v2, which excelled in translating visual features into the LLM space for single images and achieved impressive results on various image-text benchmarks, this paper extends the model's capabilities to process a sequence of frames, enabling it to comprehend videos.
• MiniGPT4-video does not only consider visual content but also incorporates textual conversations, allowing the model to effectively answer queries involving both visual and text components.