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Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow

16 May 2023arXiv:2305.09610archive 2025-07-28

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is not robust in practical data setups with distributional shifts and out-of-distribution (OOD) classes. As a result, the predicted probabilities can be very imprecise when used as confidence scores at test time. To address this, we propose a generative model for concurrent in-distribution misclassification (IDM) and OOD detection that relies on a normalizing flow framework. The proposed flow-based detector with an energy-based inputs (FlowEneDet) can extend previously deployed segmentation models without their time-consuming retraining. Our FlowEneDet results in a low-complexity architecture with marginal increase in the memory footprint. FlowEneDet achieves promising results on Cityscapes, Cityscapes-C, FishyScapes and SegmentMeIfYouCan benchmarks in IDM/OOD detection when applied to pretrained DeepLabV3+ and SegFormer semantic segmentation models.

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Code

gudovskiy/flowenedet officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Anomaly DetectionOut-of-Distribution DetectionSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection Fishyscapes FlowEneDet AP 67.8 #6 of 8 Archive leaderboard report
Anomaly Detection Fishyscapes FlowEneDet FPR95 21.58 #6 of 8 Archive leaderboard report
Anomaly Detection Fishyscapes L&F FlowEneDet AP 50.15 #5 of 18 Archive leaderboard report
Anomaly Detection Fishyscapes L&F FlowEneDet FPR95 5.20 #5 of 18 Archive leaderboard report

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Methods

ConvolutionDense ConnectionsLinear LayerMix-FFNResidual ConnectionSegFormerTest

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