MSeg is a multi-domain semantic segmentation composite dataset. This dataset unifies semantic segmentation datasets from different fields: COCO, ADE20K, Mapillary, IDD, BDD, Cityscapes, and SUN RGB-D. By coordinating classification, merging, and splitting classes, a unified classification with 194 categories is obtained. To make the pixel-level annotations conform to a unified taxonomy, we performed a large-scale annotation effort on the Mechanical Turk platform and generated compatible annotations in the dataset by re-labeling object masks.

Citation

@InProceedings{MSeg_2020_CVPR, author = {Lambert, John and Liu, Zhuang and Sener, Ozan and Hays, James and Koltun, Vladlen}, title = {{MSeg}: A Composite Dataset for Multi-domain Semantic Segmentation}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2020} } @article{Lambert23tpami_MSeg, author={Lambert, John and Liu, Zhuang and Sener, Ozan and Hays, James and Koltun, Vladlen}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation}, year={2023}, volume={45}, number={1}, pages={796-810}, doi={10.1109/TPAMI.2022.3151200} }