Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis
Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models. Existing methods mostly focus on regular entangled representations to discriminate in-distribution (ID) and OOD data, neglecting the rich contextual information within images. This issue is particularly challenging for detecting near-OOD, as models with simplicity bias struggle to learn discriminative features in disentangled representations. The human visual system can use the co-occurrence of objects in the natural environment to facilitate scene understanding. Inspired by this, we propose an Object-Centric OOD detection framework that learns to capture Object CO-occurrence (OCO) pattern...
| Authors | Boyang Dai, Chaoqi Chen, Yizhou Yu |
|---|---|
| Primary category | cs.CV |
| Published | 8 May 2026 |
| Last updated at source | 18 June 2026 |
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