Unsupervised Semantic Segmentation On Potsdam 1
المقاييس
Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
اسم النموذج | Accuracy | Paper Title | Repository |
|---|---|---|---|
| HP | 82.4 | Leveraging Hidden Positives for Unsupervised Semantic Segmentation | |
| PriMaPs-EM+HP (DINO ViT-B/8) | 83.3 | Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals | |
| InfoSeg | - | InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization | - |
| STEGO | 77.0 | Unsupervised Semantic Segmentation by Distilling Feature Correspondences | |
| PriMaPs-EM (DINO ViT-B/8) | 80.5 | Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals | |
| EAGLE (DINO, ViT-B/8) | 83.3 | EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation | |
| EQUSS | 82.0 | Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization | - |
| IIC | 45.4 | Invariant Information Clustering for Unsupervised Image Classification and Segmentation |
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