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Models/DCF

DCF

Reported on 39 benchmarks across 12 tasks · 2 papers · 15 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical15 results

Image GenerationonGTAV-to-Cityscapes Labels
mIoU· 2023-11-21
77.7
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Image GenerationonSYNTHIA-to-Cityscapes
MIoU (13 classes)· uses extra data· 2023-11-21
75.9
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Image GenerationonSYNTHIA-to-Cityscapes
MIoU (16 classes)· uses extra data· 2023-11-21
69.3
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Dice
0.325
best: 0.9 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
S measure
0.523
best: 0.9 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Sensitivity
0.34
best: 83.7 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
mean E-measure
0.514
best: 93.8 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
mean F-measure
0.312
best: 93.8 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
weighted F-measure
0.27
best: 0.794 (SALI)
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Dice
0.317
best: 0.902 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
S-Measure
0.514
best: 0.894 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Sensitivity
0.364
best: 0.852 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
mean E-measure
0.522
best: 0.941 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
mean F-measure
0.303
best: 0.932 (YOLO-SAM 2)
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
weighted F-measure
0.263
best: 0.79 (SALI)

Methodology14 results

Domain AdaptationonSYNTHIA-to-Cityscapes
MIoU (16 classes)· uses extra data· 2023-11-21
69.3
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· uses extra data· 2023-11-21
75.9
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Ensemble LearningonSMS Spam Collection Data Set
Accuracy· 2021-10-10
0.9838
SOTA
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text arXiv:2110.15718
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· 2023-11-21
69.3
best: 78.1 (HALO)
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Domain AdaptationonGTA5 to Cityscapes
mIoU· 2023-11-21
77.7
best: 77.8 (HALO)
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· uses extra data· 2023-11-21
69.3
best: 78.1 (HALO)
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
3DonDSEC
mAP
25.7
best: 38 (CAFR)
3DonPKU-DDD17-Car
mAP50
83.4
best: 86.7 (CAFR)
2D ClassificationonDSEC
mAP
25.7
best: 38 (CAFR)
2D ClassificationonPKU-DDD17-Car
mAP50
83.4
best: 86.7 (CAFR)
2D Object DetectiononDSEC
mAP
25.7
best: 38 (CAFR)
2D Object DetectiononPKU-DDD17-Car
mAP50
83.4
best: 86.7 (CAFR)
16konDSEC
mAP
25.7
best: 38 (CAFR)
16konPKU-DDD17-Car
mAP50
83.4
best: 86.7 (CAFR)

Computer Vision5 results

Image-to-Image TranslationonGTAV-to-Cityscapes Labels
mIoU· 2023-11-21
77.7
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Image-to-Image TranslationonSYNTHIA-to-Cityscapes
MIoU (13 classes)· uses extra data· 2023-11-21
75.9
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Image-to-Image TranslationonSYNTHIA-to-Cityscapes
MIoU (16 classes)· uses extra data· 2023-11-21
69.3
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Object DetectiononDSEC
mAP
25.7
best: 38 (CAFR)
Object DetectiononPKU-DDD17-Car
mAP50
83.4
best: 86.7 (CAFR)

Other3 results

Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
MIoU (16 classes)· uses extra data· 2023-11-21
69.3
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU· uses extra data· 2023-11-21
69.3
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
Unsupervised Domain AdaptationonSYNTHIA-to-Cityscapes
mIoU (13 classes)· uses extra data· 2023-11-21
75.9
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682

Miscellaneous3 results

1 Image, 2*2 StitchingonGTAV-to-Cityscapes Labels
mIoU· 2023-11-21
77.7
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
1 Image, 2*2 StitchingonSYNTHIA-to-Cityscapes
MIoU (13 classes)· uses extra data· 2023-11-21
75.9
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682
1 Image, 2*2 StitchingonSYNTHIA-to-Cityscapes
MIoU (16 classes)· uses extra data· 2023-11-21
69.3
SOTA
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation arXiv:2311.12682