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CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

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CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

This is the code of:

CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation, Junsong Fan, Zhaoxiang Zhang, Tieniu Tan, Chunfeng Song, Jun Xiao, AAAI2020 [paper].

Introuduction

fig-framework

Framework of the approach. Our approach learns the cross image relationships to help weakly-supervised semantic segmentation. The CIAN Module takes features as input from two different images, extract and change information across them to augment the original features.

Prerequisite

  • Python 3.7, MXNet 1.3.1, Numpy, OpenCV, pydensecrf.
  • NVIDIA GPUs

Usage

Prepare the dataset and pretrain parameters:

Run:

./run_cian.sh

This script will automatically run the training, testing (on val set), and retraining pipeline. Checkpoints and predictions will be saved in folder CIAN/snapshot/CIAN.

Citation

If you find the code is useful, please consider citing:

@inproceedings{fan2020cian,
  title={CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation},
  author={Fan, Junsong and Zhang, Zhaoxiang and Tan, Tieniu and Song, Chunfeng and Xiao, Jun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2020}
}

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