Synthetic to real domain adaptation is a standard setup used in state-of-the-art methods recently. Below two leaderboards for GTA-V to Cityscapes and SYNTHIA to Cityscapes are shown. Each paper is represented with its nickname, venue and year of publication. The approchares used in different papers are abbrevated as "ST" for Self-training or self-supervised learning and "Adv" for adversarial learning respectively. The papers are in descending order with respect to mean Intersection over Union (mean IoU).
| Methods | Venue/Year | Approach | mean IoU | Road | Sidewalk | Building | Wall | Fence | Pole | T.Light | T.Sign | Vegitation | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | Motorcycle | Bicycle |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MLSL (Ours) | WACV-2020 | ST | 49.0 | 89.0 | 45.2 | 78.2 | 22.9 | 27.3 | 37.4 | 46.1 | 43.8 | 82.9 | 18.6 | 61.2 | 60.4 | 26.7 | 85.4 | 35.9 | 44.9 | 36.4 | 37.2 | 49.3 |
| BDL | CVPR-2019 | Adv+ST | 48.5 | 91.0 | 44.7 | 84.2 | 34.6 | 27.6 | 30.2 | 36.0 | 36.0 | 85.0 | 43.6 | 83.0 | 58.6 | 31.6 | 83.3 | 35.3 | 49.7 | 3.3 | 28.8 | 35.6 |
| PyCDA | ICCV-2019 | ST(res-38) | 48.0 | 92.3 | 49.2 | 84.4 | 33.4 | 30.2 | 33.3 | 37.1 | 35.2 | 86.5 | 36.9 | 77.3 | 63.3 | 30.5 | 86.6 | 34.5 | 40.7 | 7.9 | 17.6 | 35.5 |
| CRST | ICCV-2019 | ST | 46.8 | 84.5 | 47.7 | 74.1 | 27.9 | 22.1 | 43.8 | 46.5 | 37.8 | 83.7 | 22.7 | 56.1 | 56.8 | 26.8 | 81.7 | 22.5 | 46.2 | 27.5 | 32.3 | 47.9 |
| DPR | ICCV-2019 | Adv | 46.5 | 92.3 | 51.9 | 82.1 | 29.2 | 25.1 | 24.5 | 33.8 | 33.0 | 82.4 | 32.8 | 82.2 | 58.6 | 27.2 | 84.3 | 33.4 | 46.3 | 2.2 | 29.5 | 32.3 |
| MaxSquare | ICCV-2019 | Adv+ST | 46.4 | 89.4 | 43.0 | 82.1 | 30.5 | 21.3 | 30.3 | 34.7 | 24.0 | 85.3 | 39.4 | 78.2 | 63.0 | 22.9 | 84.6 | 36.4 | 43.0 | 5.5 | 34.7 | 33.5 |
| CBST | ECCV-2018 | ST | 46.2 | 88.0 | 56.2 | 77.0 | 27.4 | 22.4 | 40.7 | 47.3 | 40.9 | 82.4 | 21.6 | 60.3 | 50.2 | 20.4 | 83.8 | 35.0 | 51.0 | 15.2 | 20.6 | 37.0 |
| ADVENT | CVPR-2019 | Adv+ST | 45.5 | 89.4 | 33.1 | 81.0 | 26.6 | 26.8 | 27.2 | 33.5 | 24.7 | 83.9 | 36.7 | 78.8 | 58.7 | 30.5 | 84.8 | 38.5 | 44.5 | 1.7 | 31.6 | 32.4 |
| SSF-DAN | ICCV-2019 | Adv+ST | 45.4 | 90.3 | 38.9 | 81.7 | 24.8 | 22.9 | 30.5 | 37.0 | 21.2 | 84.8 | 38.8 | 76.9 | 58.8 | 30.7 | 85.7 | 30.6 | 38.1 | 5.9 | 28.3 | 36.9 |
| All_Structure | CVPR-2019 | Adv | 45.4 | 91.5 | 47.5 | 82.5 | 31.3 | 25.6 | 33.0 | 33.7 | 25.8 | 82.7 | 28.8 | 82.7 | 62.4 | 30.8 | 85.2 | 27.7 | 34.5 | 6.4 | 25.2 | 24.4 |
| CLAN | CVPR-2019 | Adv | 43.2 | 87.0 | 27.1 | 79.6 | 27.3 | 23.3 | 28.3 | 35.5 | 24.2 | 83.6 | 27.4 | 74.2 | 58.6 | 28.0 | 76.2 | 33.1 | 36.7 | 6.7 | 31.9 | 31.4 |
| SIBAN | ICCV-2019 | Adv | 42.6 | 88.5 | 35.4 | 79.5 | 26.3 | 24.3 | 28.5 | 32.5 | 18.3 | 81.2 | 40.0 | 76.5 | 58.1 | 25.8 | 82.6 | 30.3 | 34.4 | 3.4 | 21.6 | 21.5 |
| Saleh etal | ECCV-2018 | Adv | 42.5 | 79.8 | 29.3 | 77.8 | 24.2 | 21.6 | 6.9 | 23.5 | 44.2 | 80.5 | 38.0 | 76.2 | 52.7 | 22.2 | 83.0 | 32.3 | 41.3 | 27.0 | 19.3 | 27.7 |
| AdaptNet | CVPR-2018 | Adv | 42.4 | 86.5 | 36.0 | 79.9 | 23.4 | 23.3 | 23.9 | 35.2 | 14.8 | 83.4 | 33.3 | 75.6 | 58.5 | 27.6 | 73.7 | 32.5 | 35.4 | 3.9 | 30.1 | 28.1 |
| DLOW | CVPR-2019 | Adv | 42.3 | 87.1 | 33.5 | 80.5 | 24.5 | 13.2 | 29.8 | 29.5 | 26.6 | 82.6 | 26.7 | 81.8 | 55.9 | 25.3 | 78.0 | 33.5 | 38.7 | 0.0 | 22.9 | 34.5 |
| DTA | ICCV-2019 | Adv | 35.8 | 88.8 | 36.9 | 76.9 | 20.9 | 15.4 | 19.6 | 21.8 | 7.9 | 82.9 | 26.7 | 76.1 | 51.7 | 9.4 | 76.1 | 22.4 | 28.9 | 1.7 | 15.2 | 0.0 |
| Methods | Venue/Year | Approach | mean IoU | Road | Sidewalk | Building | Wall | Fence | Pole | T.Light | T.Sign | Vegitation | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | Motorcycle | Bicycle |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MLSL (Ours) | WACV-2020 | ST | 49.0 | 89.0 | 45.2 | 78.2 | 22.9 | 27.3 | 37.4 | 46.1 | 43.8 | 82.9 | 18.6 | 61.2 | 60.4 | 26.7 | 85.4 | 35.9 | 44.9 | 36.4 | 37.2 | 49.3 |
| TGCF-DA | ICCV-2019 | Adv+ST | 42.5 | 90.2 | 51.5 | 81.1 | 15.0 | 10.7 | 37.5 | 35.2 | 28.9 | 84.1 | 32.7 | 75.9 | 62.7 | 19.9 | 82.6 | 22.9 | 28.3 | 0.0 | 23.0 | 25.4 |
| Saleh etal | ECCV-2018 | Adv | 42.5 | 79.8 | 29.3 | 77.8 | 24.2 | 21.6 | 6.9 | 23.5 | 44.2 | 80.5 | 38.0 | 76.2 | 52.7 | 22.2 | 83.0 | 32.3 | 41.3 | 27.0 | 19.3 | 27.7 |
| BDL | CVPR-2019 | Adv+ST | 41.3 | 89.2 | 40.9 | 81.2 | 29.1 | 19.2 | 14.2 | 29.0 | 19.6 | 83.7 | 35.9 | 80.7 | 54.7 | 23.3 | 82.7 | 25.8 | 28.0 | 2.3 | 25.7 | 19.9 |
| SSf-DAN | ICCV-2019 | Adv+ST | 37.7 | 88.7 | 32.1 | 79.5 | 29.9 | 22.0 | 23.8 | 21.7 | 10.7 | 80.8 | 29.8 | 72.5 | 49.5 | 16.1 | 82.1 | 23.2 | 18.1 | 3.5 | 24.4 | |
| DPR | ICCV-2019 | Adv | 37.5 | 87.3 | 35.7 | 79.5 | 32.0 | 14.5 | 21.5 | 24.8 | 13.7 | 80.4 | 32.0 | 70.5 | 50.5 | 16.9 | 81.0 | 20.8 | 28.1 | 4.1 | 15.5 | 4.1 |
| PyCDA | ICCV-2019 | ST | 37.2 | 86.7 | 24.8 | 80.9 | 21.4 | 27.3 | 30.2 | 26.6 | 21.1 | 86.6 | 28.9 | 58.8 | 53.2 | 17.9 | 80.4 | 18.8 | 22.4 | 4.1 | 9.7 | 6.2 |
| CLAN | CVPR-2019 | Adv | 36.6 | 88.0 | 30.6 | 79.2 | 23.4 | 20.5 | 26.1 | 23.0 | 14.8 | 81.6 | 34.5 | 72.0 | 45.8 | 7.9 | 80.5 | 26.6 | 29.9 | 0.0 | 10.7 | 0.0 |
| CBST | ECCV-2018 | ST | 36.1 | 90.4 | 50.8 | 72.0 | 18.3 | 9.5 | 27.2 | 28.6 | 14.1 | 82.4 | 25.1 | 70.8 | 42.6 | 14.5 | 76.9 | 5.9 | 12.5 | 1.2 | 14.0 | 28.6 |
| ADVENT | CVPR-2019 | Adv | 36.1 | 86.9 | 28.7 | 78.7 | 28.5 | 25.2 | 17.1 | 20.3 | 10.9 | 80.0 | 26.4 | 70.2 | 47.1 | 8.4 | 81.5 | 26.0 | 17.2 | 18.9 | 11.7 | 1.6 |
| AdaptNet | CVPR-2018 | Adv(sigle level) | 35.0 | 87.3 | 29.8 | 78.6 | 21.1 | 18.2 | 22.5 | 21.5 | 11.0 | 79.7 | 29.6 | 71.3 | 46.8 | 6.5 | 80.1 | 23.0 | 26.9 | 0.0 | 10.6 | 0.3 |
| SIBAN | ICCV-2019 | Adv | 34.2 | 83.4 | 13.0 | 77.8 | 20.4 | 17.5 | 24.6 | 22.8 | 9.6 | 81.3 | 29.6 | 77.3 | 42.7 | 10.9 | 76.0 | 22.8 | 17.9 | 5.7 | 14.2 | 2.0 |
| Methods | Venue/Year | Approach | mean IoU | mean IoU* | Road | Sidewalk | Building | Wall | Fence | Pole | T.Light | T.Sign | Vegitation | Sky | Person | Rider | Car | Bus | Motorcycle | Bicycle |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BDL | CVPR-2019 | Adv+ST | - | 51.4 | 86.0 | 46.7 | 80.3 | - | - | - | 14.1 | 11.6 | 79.2 | 81.3 | 54.1 | 27.9 | 73.7 | 42.2 | 25.7 | 45.3 |
| PyCDA | ICCV-2019 | ST(rses-101) | 46.7 | 53.3 | 75.5 | 30.9 | 83.3 | 20.8 | 0.7 | 32.7 | 27.3 | 33.5 | 84.7 | 85.0 | 64.1 | 25.4 | 85.0 | 45.2 | 21.2 | 32.0 |
| MLSL (Ours) | WACV-2020 | ST | 45.2 | 51.0 | 59.2 | 30.2 | 68.5 | 22.9 | 1.0 | 36.2 | 32.7 | 28.3 | 86.2 | 75.4 | 68.6 | 27.7 | 82.7 | 26.3 | 24.3 | 52.7 |
| CRST | ICCV-2019 | ST | 43.8 | 50.1 | 67.7 | 32.2 | 73.9 | 10.7 | 1.6 | 37.4 | 22.2 | 31.2 | 80.8 | 80.5 | 60.8 | 29.1 | 82.8 | 25.0 | 19.4 | 45.3 |
| SSf-DAN | ICCV-2019 | Adv+ST | - | 50.0 | 84.6 | 41.7 | 80.8 | - | - | - | 11.5 | 14.7 | 80.8 | 85.3 | 57.5 | 21.6 | 82.0 | 36.0 | 19.3 | 34.5 |
| DADA | ICCV-2019 | Adv | 42.6 | 49.8 | 89.2 | 44.8 | 81.4 | 6.8 | 0.3 | 26.2 | 8.6 | 11.1 | 81.8 | 84.0 | 54.7 | 19.3 | 79.7 | 40.7 | 14.0 | 38.8 |
| CBST | ECCV-2018 | ST | 42.5 | 48.4 | 53.6 | 23.7 | 75.0 | 12.5 | 0.3 | 36.4 | 23.5 | 26.3 | 84.8 | 74.7 | 67.2 | 17.5 | 84.5 | 28.4 | 15.2 | 55.8 |
| All_Structure | CVPR-2019 | Adv | 41.5 | 48.7 | 91.7 | 53.5 | 77.1 | 2.5 | 0.2 | 27.1 | 6.2 | 7.6 | 78.4 | 81.2 | 55.8 | 19.2 | 82.3 | 30.3 | 17.1 | 34.3 |
| MaxSquare | ICCV-2019 | Adv+ST | 41.4 | 48.2 | 82.9 | 40.7 | 80.3 | 10.2 | 0.8 | 25.8 | 12.8 | 18.2 | 82.5 | 82.2 | 53.1 | 18.0 | 79.0 | 31.4 | 10.4 | 35.6 |
| ADVENT | CVPR-2019 | Adv+ST | 41.2 | 48.0 | 85.6 | 42.2 | 79.7 | 8.7 | 0.4 | 25.9 | 5.4 | 8.1 | 80.4 | 84.1 | 57.9 | 23.8 | 73.3 | 36.4 | 14.2 | 33.0 |
| CLAN | CVPR-2019 | Adv | - | 47.8 | 81.3 | 37.0 | 80.1 | - | - | - | 16.1 | 13.7 | 78.2 | 81.5 | 53.4 | 21.2 | 73.0 | 32.9 | 22.6 | 30.7 |
| AdaptNet | CVPR-2018 | Adv | - | 46.7 | 84.3 | 42.7 | 77.5 | - | - | - | 4.7 | 7.0 | 77.9 | 82.5 | 54.3 | 21.0 | 72.3 | 32.2 | 18.9 | 32.3 |
| SIBAN | ICCV-2019 | Adv | - | 46.3 | 82.5 | 24.0 | 79.4 | - | - | - | 16.5 | 12.7 | 79.2 | 82.8 | 58.3 | 18.0 | 79.3 | 25.3 | 17.6 | 25.9 |
| DPR | ICCV-2019 | Adv | 40.0 | 46.5 | 82.4 | 38.0 | 78.6 | 8.7 | 0.6 | 26.0 | 3.9 | 11.1 | 75.5 | 84.6 | 53.5 | 21.6 | 71.4 | 32.6 | 19.3 | 31.7 |
| Methods | Venue/Year | Approach | mean IoU | mean IoU* | Road | Sidewalk | Building | Wall | Fence | Pole | T.Light | T.Sign | Vegitation | Sky | Person | Rider | Car | Bus | Motorcycle | Bicycle |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MLSL (Ours) | WACV-2020 | ST | 45.2 | 51.0 | 59.2 | 30.2 | 68.5 | 22.9 | 1.0 | 36.2 | 32.7 | 28.3 | 86.2 | 75.4 | 68.6 | 27.7 | 82.7 | 26.3 | 24.3 | 52.7 |
| BDL | CVPR-2019 | Adv+ST | 39.0 | - | 72.0 | 30.3 | 74.5 | 0.1 | 0.3 | 24.6 | 10.2 | 25.2 | 80.5 | 80.0 | 54.7 | 23.2 | 72.7 | 24.0 | 7.5 | 44.9 |
| TGCF-DA | ICCV-2019 | Adv+ST | 38.5 | 46.6 | 90.1 | 48.6 | 80.7 | 2.2 | 0.2 | 27.2 | 3.2 | 14.3 | 82.1 | 78.4 | 54.4 | 16.4 | 82.5 | 12.3 | 1.7 | 21.8 |
| SSf-DAN | ICCV-2019 | Adv+ST | - | 43.4 | 87.1 | 36.5 | 79.7 | - | - | - | 13.5 | 7.8 | 81.2 | 76.7 | 50.1 | 12.7 | 78.0 | 35.0 | 4.6 | 1.6 |
| GIO-Ada | CVPR-2019 | Adv | 37.3 | 43.0 | 78.3 | 29.2 | 76.9 | 11.4 | 0.3 | 26.5 | 10.8 | 17.2 | 81.7 | 81.9 | 45.8 | 15.4 | 68.0 | 15.9 | 7.5 | 30.4 |
| PyCDA | ICCV-2019 | ST | 35.9 | 42.6 | 80.6 | 26.6 | 74.5 | 2.0 | 0.1 | 18.1 | 13.7 | 14.2 | 80.8 | 71.0 | 48.0 | 19.0 | 72.3 | 22.5 | 12.1 | 18.1 |
| CBST | ECCV-2018 | ST | 35.4 | 36.1 | 69.6 | 28.7 | 69.5 | 12.1 | 0.1 | 25.4 | 11.9 | 13.6 | 82.0 | 81.9 | 49.1 | 14.5 | 66.0 | 6.6 | 3.7 | 32.4 |
| DPR | ICCV-2019 | Adv | 33.7 | 39.6 | 72.6 | 29.5 | 77.2 | 3.5 | 0.4 | 21.0 | 1.4 | 7.9 | 73.3 | 79.0 | 45.7 | 14.5 | 69.4 | 19.6 | 7.4 | 16.5 |
| CLAN | CVPR-2019 | Adv | - | 39.3 | 80.4 | 30.7 | 74.7 | - | - | - | 1.4 | 8.0 | 77.1 | 79.0 | 46.5 | 8.9 | 73.8 | 18.2 | 2.2 | 9.9 |
| AdaptNet | CVPR-2018 | Adv | - | 37.6 | 78.9 | 29.2 | 75.5 | - | - | - | 0.1 | 4.8 | 72.6 | 76.7 | 43.4 | 8.8 | 71.1 | 16.0 | 3.6 | 8.4 |
| SIBAN | ICCV-2019 | Adv | - | 37.2 | 70.1 | 25.7 | 80.9 | - | - | - | 3.8 | 7.2 | 72.3 | 80.5 | 43.3 | 5.0 | 73.3 | 16.0 | 1.7 | 3.6 |
| ADVENT | CVPR-2019 | Adv | 31.4 | 36.6 | 67.9 | 29.4 | 71.9 | 6.3 | 0.3 | 19.9 | 0.6 | 2.6 | 74.9 | 74.9 | 35.4 | 9.6 | 67.8 | 21.4 | 4.1 | 15.5 |
Where mean IoU* is over 13-classes (excluding wall, pole and fence).
An Implementation to our WACV-2020 paper "MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling" will be shortly available here.
If you found this useful, please cite our paper.
@article{iqbal2019mlsl,
title={MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and
Semantically Consistent Labeling},
author={Javed Iqbal and Mohsen Ali},
journal={arXiv preprint arXiv:1909.13776},
year={2019}
}
Feel free to contact for adding your published results to leaderboard.
Contact: javed.iqbal@itu.edu.pk