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تسجيل الدخول
تسجيل الدخول
الرئيسية
SOTA
Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation On
Weakly Supervised Semantic Segmentation On
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Mean IoU
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اسم النموذج
Mean IoU
Paper Title
Repository
SIPE (DeepLabV2-ResNet101, no saliency)
68.8
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation
PMM(ResNet38)
68.5
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
LIID (ResNet-101)
66.5
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
URN(ResNet-38, no saliency, no RW)
69.4
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
LIID (Res2Net-101)
69.4
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
AMN (DeepLabV2-ResNet101)
69.5
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds
URN(ScaleNet-101, no saliency, no RW)
70.1
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
SEAM+CONTA
66.1
Causal Intervention for Weakly-Supervised Semantic Segmentation
ISIM (ResNet-101)
70.51
ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation
LIID (ResNet-101, +24K SI)
67.8
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
WeakTr (DeiT-S, multi-stage)
74.0
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
ResNet-101
66.2
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
SGAN
67.1
Saliency Guided Self-attention Network for Weakly and Semi-supervised Semantic Segmentation
ACR-WSSS(DeepLabV1-ResNet101)
71.2
All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation
Infer-CAM(DeepLabV2-ResNet101)
70.8
Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation
PMM(ResNet38, no saliency, no RW)
68.5
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
SemPLeS (Swin-L)
83.4
Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
VWL-L
70.6
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
PMM(Res2Net101, no saliency, no RW)
70.0
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
OC-CSE(ResNet38,no saliency)
68.4
Unlocking the Potential of Ordinary Classifier: Class-Specific Adversarial Erasing Framework for Weakly Supervised Semantic Segmentation
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