VideoMAEv2 VS MiniGPT-4

Thanks for your interest! If you had any ideas to make the given demo more user-friendly, please do not hesitate to share them with us. We are open to discussing relevant ideas about video foundation models or other topics. We made some progress in these areas (InternVideo, VideoMAE v2, UMT, and more). We believe that user-level intelligent video understanding is on the horizon with the current LLM, computing power, and video data.

I just think the tech has been out for so long it's not as big of a deal. Mini-Gpt4 has been out for 6 months! Of course the descriptions aren't exactly gpt-4 grade, but with mistral 7b being used as the language model instead of llama 7b, the reasoning ability will improve noticeably.

[1] https://github.com/Vision-CAIR/MiniGPT-4

Isn't there only two open multimodal LLMs, LLaVA and mini-gpt4?

For LLM visual instruction, you can use LLaVA, LaVIN, or MiniGPT-4.

Please read the rules before posting. If you want a model for visual instruction, use LLaVA, LaVIN, or MiniGPT-4.

So we use MiniGPT-4 for image parsing, and yep it does return a pretty detailed (albeit not always accurate) description of the photo. You can actually play around with it on Huggingface here.

We use MiniGPT-4 first to interpret the image and then pass the results onto GPT-4. Hopefully, once GPT-4 makes its multi-modal functionality available, we can do it all in one request.

But I would like to bring up that there are some multi models(llava, miniGPT-4) that are built based on censored llama based models like vicuna. I tried several multi modal models like llava, minigpt4 and blip2. Llava has very good captioning and question answering abilities and it is also much faster than the others(basically real time), though it has some hallucination issue.