Divide and Conquer: Object Co-occurrence Helps Mitigate Simplicity Bias in OOD Detection

ArXi:2605.07821v1 Announce Type: cross 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.