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Models/RPL+CoroCL

RPL+CoroCL

Reported on 6 benchmarks across 1 task · 1 paper · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology6 results

Anomaly DetectiononFishyscapes
AP· uses extra data· 2022-11-26
95.96
SOTA
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation arXiv:2211.14512
Anomaly DetectiononRoad Anomaly
AP· uses extra data· 2022-11-26
71.61
best: 95.21 (OodDINO)
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation arXiv:2211.14512
Anomaly DetectiononRoad Anomaly
FPR95· uses extra data· 2022-11-26
17.74
best: 64.69 (SynthCP)
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation arXiv:2211.14512
Anomaly DetectiononFishyscapes
FPR95· uses extra data· 2022-11-26
0.52
best: 21.58 (FlowEneDet)
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation arXiv:2211.14512
Anomaly DetectiononFishyscapes L&F
AP· uses extra data· 2022-11-26
53.99
best: 69.8 (cDNP+OE)
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation arXiv:2211.14512
Anomaly DetectiononFishyscapes L&F
FPR95· uses extra data· 2022-11-26
2.27
best: 47.43 (Dirichlet DeepLab)
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation arXiv:2211.14512