mlsanityData to 7 Oct 2026

MOODv2: Masked Image Modeling for Out-of-Distribution Detection

arXiv 2401.02611 ↗First posted 5 Jan 2024cs.CVcited by 6

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Abstract (arXiv)

The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples. While previous methods predominantly leaned on recognition-based techniques for this purpose, they often resulted in shortcut learning, lacking comprehensive representations. In our study, we conducted a comprehensive analysis, exploring distinct pretraining tasks and employing various OOD score functions. The results highlight that the feature representations pre-trained through reconstruction yield a notable enhancement and narrow the performance gap among various score functions. This suggests that even simple score functions can rival complex ones when leveraging reconstruction-based pretext tasks. Reconstruction-based pretext tasks adapt well to various score functions. As such, it holds promising potential for further expansion. Our OOD detection framework, MOODv2, employs the masked image modeling pretext task. Without bells and whistles, MOODv2 impressively enhances 14.30% AUROC to 95.68% on ImageNet and achieves 99.98% on CIFAR-10.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 5 Jan 2024.

Builds on

31 of 47 references.

References · 31

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19 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 4.

6 papers.

  1. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 24
  2. 2025Resilient Contrastive Pre-training under Non-Stationary Driftcited by 7
  3. 2025Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimationcited by 3
  4. 2025DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detectioncited by 1
  5. 2024CIC: Circular Image Compressioncited by 0
  6. 2025U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cordcited by 0