Weakly Supervised Instance Segmentation On
Metrics
Results
Performance results of various models on this benchmark
Model Name | Average Best Overlap | Paper Title | Repository | |||
|---|---|---|---|---|---|---|
| BESTIE (point label, proposal-free) | - | 66.4 | 56.1 | 30.2 | Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement | - |
| BBAM | 63.0 | 76.8 | 63.7 | 31.8 | BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation | - |
| WSIS_CL | 48.2 | 56.6 | 38.1 | 12.3 | Weakly Supervised Instance Segmentation by Deep Community Learning | - |
| BESTIE (image-level label, proposal-free) | - | 61.2 | 51.0 | 26.6 | Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement | - |
| LIID | 50.8 | - | 48.4 | 24.9 | Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation | |
| CIM + Mask R-CNN | - | 68.7 | 55.9 | 30.9 | Complete Instances Mining for Weakly Supervised Instance Segmentation | - |
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