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Cross-Modal Self-Attention Network for Referring Image Segmentation

This repository contains code and trained model for the paper "Cross-Modal Self-Attention Network for Referring Image Segmentation", CVPR 2019.

If you find this code or pre-trained models useful, please cite the following papers:

@inproceedings{ye2019cross,
  title={Cross-Modal Self-Attention Network for Referring Image Segmentation},
  author={Ye, Linwei and Rochan, Mrigank and Liu, Zhi and Wang, Yang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={10502--10511},
  year={2019}
}

Requirement

• Python 2.7
• Tensorflow 1.2 or higher
• PyDenseCRF

Setup

Partial coda and data preparation are borrowed from TF-phrasecut-public. Please follow their instructions to make your setup ready. DeepLab backbone network is based on tensorflow-deeplab-resnet as well as the pretrained model for initializing weights of our model.

Sample code

Training

python main_cmsa.py -m train -w deeplab -d Gref -t train -g 0 -i 800000

Testing

python main_cmsa.py -m test -w deeplab -d Gref -t val -g 0 -i 800000

A trained model is available here. You should be able to produce results on Gref validation dataset as 39.96% / 40.07% (without/with CRF) in terms of IoU.