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Code for ECCV2018 - Learning Human-Object Interactions by Graph Parsing Neural Networks.

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ECCV2018 - Learning Human-Object Interactions by Graph Parsing Neural Networks


The algorithm is described in the ECCV 2018 paper: Learning Human-Object Interactions by Graph Parsing Neural Networks. In this work, we introduce the Graph Parsing Neural Network (GPNN), a framework that incorporates structural knowledge while being differentiable end-to-end. teaser

Environment and installation

This repository is developed under CUDA8.0 and pytorch3.1 in python2.7. Early versions of pytorch can be found here. The required python packages can be installed by:

pip install
pip install -r requirements.txt

Download the pre-trained model and features

  • Download the entire
    folder from this google drive and put under the root folder of the repository.
  • Unzip the
    file under
  • The file structure should look like:

Running the code

  • Change the
    to your own path of the repository.
  • Run the following files for experiments:
    human-object interaction detection for HICO-DET dataset.
    : detection for the CAD120 dataset.
    : prediction for the CAD120 dataset.

If you want to train the model from scratch, just change the default epoch number from 0 to 100, and rename the pre-trained models. Running the above files should start the training.


The experiment results are provided in

. The benchmarking tool for the DET-HICO datset can be found here.


If you find this code useful, please cite our work with the following bibtex:

    title={Learning Human-Object Interactions by Graph Parsing Neural Networks},
    author={Qi, Siyuan and Wang, Wenguan and Jia, Baoxiong and Shen, Jianbing and Zhu, Song-Chun},
    booktitle={European Conference on Computer Vision (ECCV)},

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