Papers › Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
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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Code
Syntology Ran 7 of 14 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 2 ran · honoured contract; 3 ran · our draft was wrong; 2 ran with no contract checked.
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Code Syntology ran Syntology
14 samples harvested; 7 ran; 2 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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