mlsanityData to 7 Oct 2026

Hybrid Open-set Segmentation with Synthetic Negative Data

arXiv 2301.08555 ↗First posted 19 Jan 2023cs.CVcited by 8

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Abstract (arXiv)

Open-set segmentation can be conceived by complementing closed-set classification with anomaly detection. Many of the existing dense anomaly detectors operate through generative modelling of regular data or by discriminating with respect to negative data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose a novel anomaly score that fuses generative and discriminative cues. Our score can be implemented by upgrading any closed-set segmentation model with dense estimates of dataset posterior and unnormalized data likelihood. The resulting dense hybrid open-set models require negative training images that can be sampled from an auxiliary negative dataset, from a jointly trained generative model, or from a mixture of both sources. We evaluate our contributions on benchmarks for dense anomaly detection and open-set segmentation. The experiments reveal strong open-set performance in spite of negligible computational overhead.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 73 of its 99 references that are in the map. First posted on arXiv 19 Jan 2023.

Builds on

73 of 99 references.

References · 73

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50 most cited of 73 All 73 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 2.

8 papers.

  1. 2023Maskomaly:Zero-Shot Mask Anomaly Segmentationcited by 34
  2. 2024Outlier detection by ensembling uncertainty with negative objectnesscited by 34
  3. 2024FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentationcited by 2
  4. 2025A Dataset for Semantic Segmentation in the Presence of Unknownscited by 2
  5. 2023Real time dense anomaly detection by learning on synthetic negative datacited by 0
  6. 2025Language-Guided Open-World Anomaly Segmentationcited by 0
  7. 2026Energy-Aware NECO for Single-Pass Pixel-wise Out-of-Distribution Detection in Semantic Segmentationcited by 0
  8. 2026SAFe: Segment-guided Aggregation of Feature Densities for Anomaly-aware Segmentationcited by 0