Anomaly Detection On Fishyscapes L F

Metrics

AP

FPR95

Results

Performance results of various models on this benchmark

			Paper Title
cDNP+OE	69.8	7.5	Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
NFlowJS-GF (with extra inlier set: Vistas and Wilddash2)	69.43	2.00	Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
cDNP	62.2	8.9	Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
RPL+CoroCL	53.99	2.27	Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
FlowEneDet	50.15	5.20	Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
Mask2Anomaly	46.04	4.36	Unmasking Anomalies in Road-Scene Segmentation
PEBAL	44.17	7.58	Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
DenseHybrid	43.9	6.2	DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
SynBoost	43.22	15.79	Pixel-wise Anomaly Detection in Complex Driving Scenes
CosMe	41.95	13.32	Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
NFlow	39.36	-	Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
SML	36.55	14.53	Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
Dirichlet DeepLab	34.28	47.43	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
OutlierHead combined instances	31.31	19.02	Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
Void Classifier	10.29	22.11	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Bayesian DeepLab	9.8	38.5	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Learned Embedding Density	4.7	24.4	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Softmax Entropy	2.9	44.8	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

0 of 18 row(s) selected.

Anomaly Detection On Fishyscapes L F

Metrics

AP

FPR95

Results

Performance results of various models on this benchmark

			Paper Title
cDNP+OE	69.8	7.5	Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
NFlowJS-GF (with extra inlier set: Vistas and Wilddash2)	69.43	2.00	Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
cDNP	62.2	8.9	Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
RPL+CoroCL	53.99	2.27	Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
FlowEneDet	50.15	5.20	Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
Mask2Anomaly	46.04	4.36	Unmasking Anomalies in Road-Scene Segmentation
PEBAL	44.17	7.58	Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
DenseHybrid	43.9	6.2	DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
SynBoost	43.22	15.79	Pixel-wise Anomaly Detection in Complex Driving Scenes
CosMe	41.95	13.32	Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
NFlow	39.36	-	Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
SML	36.55	14.53	Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
Dirichlet DeepLab	34.28	47.43	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
OutlierHead combined instances	31.31	19.02	Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
Void Classifier	10.29	22.11	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Bayesian DeepLab	9.8	38.5	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Learned Embedding Density	4.7	24.4	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Softmax Entropy	2.9	44.8	The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

0 of 18 row(s) selected.

Anomaly Detection On Fishyscapes L F | SOTA | HyperAI