Papers › Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
Jingyao Li, Pengguang Chen, Shaozuo Yu, Zexin He, Shu Liu, Jiaya Jia
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-based methods to learn the ID features, which tend to learn shortcuts instead of comprehensive representations. In this work, we find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We deeply explore the main contributors of OOD detection and find that reconstruction-based pretext tasks have the potential to provide a generally applicable and efficacious prior, which benefits the model in learning intrinsic data distributions of the ID dataset. Specifically, we take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD). Without bells and whistles, MOOD outperforms previous SOTA of one-class OOD detection by 5.7%, multi-class OOD detection by 3.0%, and near-distribution OOD detection by 2.1%. It even defeats the 10-shot-per-class outlier exposure OOD detection, although we do not include any OOD samples for our detection
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Out-of-Distribution Detection | ImageNet-1k vs Curated OODs (avg.) | MOOD | AUROC | 89.1 | #16 of 16 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs Places | MOOD | AUROC | 88.5 | #25 of 25 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs SUN | MOOD | AUROC | 89.8 | #22 of 22 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs Textures | MOOD | AUROC | 91.3 | #18 of 34 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs iNaturalist | MOOD | AUROC | 86.9 | #21 of 28 | 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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