Salient Object Detection On Duts Te 1
Métriques
E-measure
MAE
Smeasure
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | E-measure | MAE | Smeasure | Paper Title | Repository |
|---|---|---|---|---|---|
| SAM2-UNet | 0.959 | 0.020 | 0.934 | SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation | - |
| F3Net | 0.901 | 0.035 | 0.888 | F3Net: Fusion, Feedback and Focus for Salient Object Detection | - |
| LDF(ResNet-50) | 0.909 | 0.034 | 0.892 | Label Decoupling Framework for Salient Object Detection | - |
| SelfReformer | 0.920 | 0.026 | 0.911 | SelfReformer: Self-Refined Network with Transformer for Salient Object Detection | - |
| RCSB | 0.903 | 0.034 | 0.878 | Recursive Contour Saliency Blending Network for Accurate Salient Object Detection | - |
| SelfReformer-Swin | 0.924 | 0.024 | 0.921 | SelfReformer: Self-Refined Network with Transformer for Salient Object Detection | - |
| EVPv2 | 0.948 | 0.027 | 0.915 | Explicit Visual Prompting for Universal Foreground Segmentations | - |
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