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プラットフォーム
ホーム
SOTA
非監督ドメイン適応
Unsupervised Domain Adaptation On Gtav To
Unsupervised Domain Adaptation On Gtav To
評価指標
mIoU
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
mIoU
Paper Title
MIC
75.9
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
HRDA + PiPa
75.6
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
CLUDA+HRDA
74.4
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
HRDA
73.8
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
DAFormer + PiPa
71.7
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
DIDA
71.0
Dual-level Interaction for Domain Adaptive Semantic Segmentation
SePiCo
70.3
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
CAMix (w DAFormer)
70.0
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
DAFormer + ProCST
69.4
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
DAFormer
68.3
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
TransDA-B
63.9
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
BiSMAP (ResNet 101)
61.2
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation
G2L
59.7
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation
FAFS
58.8
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment
Re-EnD-UDA
57.98
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
CAMix (w Deeplabv2 ResNet 101)
55.2
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
Uncertainty + Adaboost
50.9
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
Uncertainty
50.3
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
BiMaL
47.3
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
MRNet
45.5
Unsupervised Scene Adaptation with Memory Regularization in vivo
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