Official Implementation of Few-shot Visual Relationship Co-localization

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Deep Learning VRC
Overview

VRC

Official implementation of the Few-shot Visual Relationship Co-localization (ICCV 2021) paper

project page | paper

Requirements

To setup environment

# create new env vrc
$ conda create -n vrc python=3.8.5

# activate vrc
$ conda activate vrc

# install pytorch, torchvision
$ conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch

# install other dependencies
$ pip install -r requirements.txt

Training

Preparing dataset

Training VR Encoder (VTransE)

Training parameters

To check and update training, model and dataset parameters see VR_Encoder/configs

To train VR Encoder:

$ python train_vr_encoder.py

Training VR Similarity Network (Relation Network)

Training parameters

To check and update training, testing, model and dataset parameters see VR_SimilarityNetwork/configs

To train VR Similarity Network:

$ python SimilarityNetworkTrain.py

To train VR Similarity Network (w/ concat as VR Encoding):

$ python ConcatplusSimilarityNetworkTrain.py

To evaluate (set eval setting in test_config.yaml)

$ python FullModelTest.py

Cite

If you find this code/paper useful for your research, please consider citing.

@InProceedings{teotiaMMM2021,
  author    = "Teotia, Revant and Mishra, Vaibhav and Maheshwari, Mayank and Mishra, Anand",
  title     = "Few-shot Visual Relationship Co-Localization",
  booktitle = "ICCV",
  year      = "2021",
}

Acknowledgements

This repo uses https://gitlab.com/meetshah1995/vqa-maskrcnn-benchmark and scripts from https://github.com/facebookresearch/mmf for Faster R-CNN feature extraction.

Code provided by https://github.com/zawlin/cvpr17_vtranse and https://github.com/yangxuntu/vrd helped in implementing VR encoder.

Contact

For any clarification, comment, or suggestion please create an issue or contact Revant, Vaibhav or Mayank.

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