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SOTA
Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation On
Weakly Supervised Semantic Segmentation On
Métriques
Mean IoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
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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