[Demo] Watch Videos with ChatGPT

• Ask-Anything

  3 2,685 8.1 Python

  [CVPR2024 Highlight][VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.

  

The project currently only has a basic framework and includes two main subprojects by leveraging existing APIs and open-sourced solutions:

• MiniGPT-4

  37 24,899 9.1 Python

  Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)

  

Video MiniGPT-4: It implicitly encodes videos into features and feeds it into Vicuna to achieve simple Q&A. Currently, a video prompt based on MiniGPT-4 has been introduced. Since no training has been used in our project, it is insensitive to timing and the effect needs improvement.

• InfluxDB

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  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• FastChat

  83 34,277 9.6 Python

  An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.

  

Video MiniGPT-4: It implicitly encodes videos into features and feeds it into Vicuna to achieve simple Q&A. Currently, a video prompt based on MiniGPT-4 has been introduced. Since no training has been used in our project, it is insensitive to timing and the effect needs improvement.

• LLaVA

  20 16,333 9.3 Python

  [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

In terms of effectiveness, VideoChat can cover most Q&A, but it is still imperfect. Q&A heavily relies on explicitly encoding video text and requires delicate prompt design. Also, the inference cost is high, and there is a long way to go before the actual application. Recently, implicit encoding explored by BLIP2, MiniGPT-4, and LLaVA has shown a sound and imaginative direction.

• InternVideo

  3 933 8.0 Python

  Video Foundation Models & Data for Multimodal Understanding

  

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.

• VideoMAEv2

  1 397 4.1 Python

  [CVPR 2023] VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

  

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.

• unmasked_teacher

  1 244 6.9 Python

  [ICCV2023 Oral] Unmasked Teacher: Towards Training-Efficient Video Foundation Models

  

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.

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  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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