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Maskomaly:Zero-Shot Mask Anomaly Segmentation

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arxiv 2305.16972 v2 pith:UW7VY2VB submitted 2023-05-26 cs.CV

Jan Ackermann , Christos Sakaridis , Fisher Yu This is my paper
classification cs.CV
keywords maskomalysegmentationanomalymasknetworksrequireroadanomalysimple
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a simple and practical framework for anomaly segmentation called Maskomaly. It builds upon mask-based standard semantic segmentation networks by adding a simple inference-time post-processing step which leverages the raw mask outputs of such networks. Maskomaly does not require additional training and only adds a small computational overhead to inference. Most importantly, it does not require anomalous data at training. We show top results for our method on SMIYC, RoadAnomaly, and StreetHazards. On the most central benchmark, SMIYC, Maskomaly outperforms all directly comparable approaches. Further, we introduce a novel metric that benefits the development of robust anomaly segmentation methods and demonstrate its informativeness on RoadAnomaly.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detection

    cs.CV 2025-08 conditional novelty 6.0 of 10

    An evidential classifier trained on synthetic outliers reduces the average false-positive rate to 2.5% for pixel-wise out-of-distribution detection in road-scene segmentation.

  2. Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Road-query polygons plus CLIP zero-shot filtering turn Mask2Former soft masks into stronger training-free road anomaly segmentations, topping Maskomaly AP on Fishyscapes LostAndFound.

  3. Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation

    cs.RO 2025-05 conditional novelty 5.0 of 10

    A test-time diffusion editing pipeline that erases anomalies from images and flags the edited regions via CLIP feature differences detects pixel-level out-of-distribution objects in off-road scenes.

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