Anomaly Detection On Road Anomaly
评估指标
AP
FPR95
评测结果
各个模型在此基准测试上的表现结果
模型名称 | AP | FPR95 | Paper Title | Repository |
|---|---|---|---|---|
| Mask2Anomaly | 79.70 | 13.45 | Unmasking Anomalies in Road-Scene Segmentation | - |
| RbA | 90.28 | 4.92 | RbA: Segmenting Unknown Regions Rejected by All | - |
| Synboost | 41.83 | 59.72 | Pixel-wise Anomaly Detection in Complex Driving Scenes | - |
| DOoD | 89.1 | 8.8 | Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond | - |
| SML | 25.82 | 49.74 | Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation | - |
| cDNP | 85.6 | 9.8 | Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection | - |
| PEBAL | 45.10 | 44.58 | Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes | - |
| SynthCP | 24.86 | 64.69 | Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation | - |
| RPL+CoroCL | 71.61 | 17.74 | Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation | - |
| OodDINO | 95.21 | 2.11 | - | - |
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