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z-x-yang / DoraemonGPT

Official repository of DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models
BSD 3-Clause "New" or "Revised" License
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DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models
(Exemplified as A Video Agent)

Zongxin Yang  Guikun Chen  Xiaodi Li  Wenguan Wang  Yi Yang✉ 
ReLER, CCAI, Zhejiang University
✉Corresponding Author
ICML 2024 (arXiv Preprint)
Project Page
Overview. Given a video with a question/task, DoraemonGPT first extracts a Task-related Symbolic Memory, which has two types of memory for selection: space-dominant memory based on instances and time-dominant memory based on time frames/clips. The memory can be queried by sub-task tools, which are driven by LLMs with different prompts and generate symbolic language (i.e., SQL sentences) to do different reasoning. Also, other tools for querying external knowledge or utility tools are supported. For planning, DoraemonGPT employs the MCTS Planner to decompose the question into an action sequence by exploring multiple feasible N solutions, which can be further summarized into an informative answer.

Setup and Configuration 🛠️

Installation Steps

  1. Clone the repository 📦: 
    git clone https://github.com/z-x-yang/DoraemonGPT.git
  2. Opt for a virtual environment 🧹 and install the dependencies 🧑‍🍳: 
    pip install -r requirements.txt

  3. Set up your API key 🗝️:

    • Fill in config/inference/inference.yaml with your keys:

      openai:
GPT_API_KEY: ["put your openai key here", ...]

google_cloud:
CLOUD_VISION_API_KEY: [...]
QUOTA_PROJECT_ID: [...]

  4. Download the checkpoints and bulid related project🧩:

    Thanks for the authors of these open source projects below for providing valuable pre-training models with outstanding performance🤝. When utilizing these models, users must strictly adhere to the authors' licensing agreements and properly cite the sources in published works.

    • download the pretrained model for action recognition

      mkdir checkpoints  
cd ./checkpoints

#download the pretrained model for action recognition
wget https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/uniformerv2/k400/k400_k710_uniformerv2_b16_8x224.pyth

    • download the pretrained model for yolo-tracking

      #download the pretrained model for object detection and tracking
wget https://objects.githubusercontent.com/github-production-release-asset-2e65be/521807533/0c7608ab-094c-4c63-8c0c-3e7623db6114?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240612%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240612T083947Z&X-Amz-Expires=300&X-Amz-Signature=7b6688c64e3d3f1eb54a0eca30ca99e140bed9f886d4c8a084bec389046ecda8&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=521807533&response-content-disposition=attachment%3B%20filename%3Dyolov8n-seg.pt&response-content-type=application%2Foctet-stream
wget https://objects.githubusercontent.com/github-production-release-asset-2e65be/521807533/67360104-677c-457e-95a6-856f07ba3f2e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240612%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240612T083803Z&X-Amz-Expires=300&X-Amz-Signature=8bd5d0f9ef518ee1a84783203b2d0a6c285a703dace053ae30596c68f2428599&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=521807533&response-content-disposition=attachment%3B%20filename%3Dyolov8n.pt&response-content-type=application%2Foctet-stream
    • download the pretrained model for dense captioning 
      mkdir ./blip
cd ./blip
# dowlond the chekpoints from below 
[[Hugging Face](https://huggingface.co/Salesforce/blip-image-captioning-large/tree/main)]
cd ..
    • download the pretrained model for inpainting 
      #download the pretrained model for inpainting
mkdir ./E2FGVI
cd ./E2FGVI
# dowlond the chekpoints from below 
[[Google Drive](https://drive.google.com/file/d/1tNJMTJ2gmWdIXJoHVi5-H504uImUiJW9/view?usp=sharing)] 
[[Baidu Disk](https://pan.baidu.com/s/1qXAErbilY_n_Fh9KB8UF7w?pwd=lsjw)]
cd ..

    • download the pretrained model for rvos

      #download the pretrained model for rvos
mkdir AOT 
cd ./AOT
# dowlond the chekpoints from below 
[[Google Drive](https://drive.google.com/file/d/1QoChMkTVxdYZ_eBlZhK2acq9KMQZccPJ/view)]
cd ..

mkdir GroundedSAM
cd ./GroundedSAM
wget https://objects.githubusercontent.com/github-production-release-asset-2e65be/611591640/c4c55fde-97e5-47d9-a2c5-b169832a2fa9?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240623%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240623T053405Z&X-Amz-Expires=300&X-Amz-Signature=369fd1d480eb018f7b3a31e960835ae77ae5bb9b1d0dcc5415751811daf4e325&X-Amz-SignedHeaders=host&actor_id=97865789&key_id=0&repo_id=611591640&response-content-disposition=attachment%3B%20filename%3Dgroundingdino_swinb_cogcoor.pth&response-content-type=application%2Foctet-stream
# dowlond the chekpoints from below 
[[Github](https://github.com/ChaoningZhang/MobileSAM/blob/master/weights/mobile_sam.pt)]
cd ../..

QuickStart 🚀

News and Todo🗓️

Overview 📜

Thanks to the authors of these open source projects for providing excellent projects.

Memory Construction

Tool Usage

Citations

Please consider citing the related paper(s) in your publications if it helps your research.

@inproceedings{yang2024doraemongpt,
  title={Doraemongpt: Toward understanding dynamic scenes with large language models (exemplified as a video agent)},
  author={Yang, Zongxin and Chen, Guikun and Li, Xiaodi and Wang, Wenguan and Yang, Yi},
  booktitle={Forty-first International Conference on Machine Learning}
}

License 🏷️

This project is all yours under the MIT License.