A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

Content

This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains":

1. ADPdevkit: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website
2. VOCdevkit: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets
3. DGdevkit: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style
4. cues: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods
5. models_cnn: a folder containing the pre-trained CNN models
6. models_wsss: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings

More information

For more information, please refer to the following article. Please cite this article when using the data set.

@misc{chan2019comprehensive,
    title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains},
    author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis},
    year={2019},
    eprint={1912.11186},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

For the full code released on GitHub, please visit the repository at: https://github.com/lyndonchan/wsss-analysis

Contact

For questions, please contact:
Lyndon Chan
lyndon.chan@mail.utoronto.ca
http://orcid.org/0000-0002-1185-7961