GLAM

This repo is the unofficial implementation of paper "Joint Graph Learning and Matching for Semantic Feature Correspondence"

Install

1. create conda environment

conda create -n GLAM python=3.8 

2. conda install pytorch

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

Data Preparation

download spair71-K and unzip in /data/downloaded/

http://cvlab.postech.ac.kr/research/SPair-71k/

Run

python3 train.py ./experiments/spair.json

Credits and Citation

Please cite the following paper if you use this model in your research:

Liu H, Wang T, Li Y, et al. Joint Graph Learning and Matching for Semantic Feature Correspondence[J]. arXiv preprint arXiv:2109.00240, 2021.