MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with
  Interleaved Visual-Textual Tokens
Kirolos Ataallah Xiaoqian Shen Eslam Abdelrahman Essam Sleiman Deyao Zhu Jian Ding Mohamed Elhoseiny
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. The proposed model outperforms existing state-of-the-art methods,
registering gains of 4.22%, 1.13%, 20.82%, and 13.1% on the MSVD, MSRVTT, TGIF,
and TVQA benchmarks respectively. Our models and code have been made publicly
available here https://vision-cair.github.io/MiniGPT4-video/