Papers › Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

18 Mar 2020ECCV 2020 8arXiv:2003.08440archive 2025-07-28

Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, Alan Yuille

The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis. In this paper, we systematically study failure and anomaly detection for semantic segmentation and propose a unified framework, consisting of two modules, to address these two related problems. The first module is an image synthesis module, which generates a synthesized image from a segmentation layout map, and the second is a comparison module, which computes the difference between the synthesized image and the input image. We validate our framework on three challenging datasets and improve the state-of-the-arts by large margins, \emph{i.e.}, 6% AUPR-Error on Cityscapes, 7% Pearson correlation on pancreatic tumor segmentation in MSD and 20% AUPR on StreetHazards anomaly segmentation.

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YingdaXia/SynthCP officialmentioned in papermentioned on GitHubpytorchMIT report

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2ran · honoured contract
3ran · our draft was wrong
2ran
7unverified

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conv1x1 YingdaXia/SynthCP/models/resnet.py official repository ran · our draft was wrong MIT (permissive) · d9def42110729a85 · report
conv3x3 YingdaXia/SynthCP/models/resnet.py official repository ran · our draft was wrong MIT (permissive) · 160bb14bd76201b4 · report
conv3x3 YingdaXia/SynthCP/models/deeplab.py official repository ran · our draft was wrong MIT (permissive) · fac5364e2f53c6db · report
fpr_and_fdr_at_recall YingdaXia/SynthCP/anomaly/anom_utils.py official repository ran MIT (permissive) · 7242da839b9822be · report
get_measures YingdaXia/SynthCP/anomaly/anom_utils.py official repository ran MIT (permissive) · b9b6c36f7a3e7e67 · report
outS YingdaXia/SynthCP/models/deeplab.py official repository ran · honoured contract fingerprinted MIT (permissive) · 27504cbeb5811ea6 · report
stable_cumsum YingdaXia/SynthCP/anomaly/anom_utils.py official repository ran · honoured contract fingerprinted MIT (permissive) · d4acb3120a027622 · report
Deeplab YingdaXia/SynthCP/models/deeplab.py official repository unverified MIT (permissive) · 0d2051ae0eca8e7f · report
Deeplab_SelfConfid YingdaXia/SynthCP/models/deeplab_self_confid.py official repository unverified MIT (permissive) · 21dd2553497dab8f · report
get_upsample_filter YingdaXia/SynthCP/models/fcn8.py official repository unverified MIT (permissive) · aca6257565eaac65 · report
imresize YingdaXia/SynthCP/anomaly/dataset.py official repository unverified MIT (permissive) · e23968c493407287 · report
init_eye YingdaXia/SynthCP/models/fcn8.py official repository unverified MIT (permissive) · d75dbb96994623b5 · report
make_layers YingdaXia/SynthCP/models/fcn8.py official repository unverified MIT (permissive) · 282e1dd5518bb58f · report
resnet18 YingdaXia/SynthCP/models/resnet.py official repository unverified MIT (permissive) · df4e7d709751b62b · report

Tasks

Anomaly DetectionAnomaly SegmentationAutonomous DrivingImage GenerationMedical Image AnalysisSegmentationSemantic SegmentationTumor Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection Road Anomaly SynthCP AP 24.86 #10 of 10 Archive leaderboard report
Anomaly Detection Road Anomaly SynthCP FPR95 64.69 #10 of 10 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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