Salient Object Detection On Dut Omron 2
Metrics
E-measure
MAE
S-measure
Results
Performance results of various models on this benchmark
Model Name | E-measure | MAE | S-measure | Paper Title | Repository |
|---|---|---|---|---|---|
| SAM2-UNet | 0.912 | 0.039 | 0.884 | SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation | - |
| F3Net | 0.869 | 0.052 | 0.838 | F3Net: Fusion, Feedback and Focus for Salient Object Detection | - |
| LDF | 0.873 | 0.051 | 0.838 | Label Decoupling Framework for Salient Object Detection | - |
| SelfReformer | 0.886 | 0.041 | 0.856 | SelfReformer: Self-Refined Network with Transformer for Salient Object Detection | - |
| EVPv2 | 0.895 | 0.047 | 0.862 | Explicit Visual Prompting for Universal Foreground Segmentations | - |
| RCSB | 0.856 | 0.045 | 0.820 | Recursive Contour Saliency Blending Network for Accurate Salient Object Detection | - |
| SelfReformer-Swin | 0.884 | 0.043 | 0.859 | SelfReformer: Self-Refined Network with Transformer for Salient Object Detection | - |
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