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SOTA
Domain Adaptation
Domain Adaptation On Synthia To Cityscapes
Domain Adaptation On Synthia To Cityscapes
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mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
mIoU
Paper Title
Repository
PyCDA (VGG-16)
35.9
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
MIC + Guidance Training
63.8
Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation
CD-AM (VGG-16)
40.8
Context-Aware Domain Adaptation in Semantic Segmentation
-
MIC
67.3
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
STPL
51.8
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation
SAC (ResNet-101)
52.6
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
ProDA+CRA
56.9
Cross-Region Domain Adaptation for Class-level Alignment
-
SePiCo (DeepLabv2-ResNet-101)
58.1
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
-
LDR (VGG-16)
41.1
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
-
PIT (VGG-16)
38.1
Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer
-
HALO
78.1
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
PyCDA (ResNet-101)
46.7
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
FDA (VGG-16)
40.5
FDA: Fourier Domain Adaptation for Semantic Segmentation
FREDOM - Transformer
67
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
BiMaL
46.2
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
FADA (ResNet-101)
45.2
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
IAST (ResNet-101)
49.8
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
SePiCo
64.3
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
-
ILM-ASSL
76.6
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation
DCF
69.3
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
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