Component-Based Out-of-Distribution Detection

Wenrui Liu; Hong Chang; Ruibing Hou; Shiguang Shan; Xilin Chen

cs.CVApr 23, 2026

Component-Based Out-of-Distribution Detection

Authors: Wenrui Liu, Hong Chang, Ruibing Hou, Shiguang Shan, Xilin Chen

Abstract

Out-of-Distribution (OOD) detection requires sensitivity to subtle shifts without overreacting to natural In-Distribution (ID) diversity. However, from the viewpoint of detection granularity, global representation inevitably suppress local OOD cues, while patch-based methods are unstable due to entangled spurious-correlation and noise. And neither them is effective in detecting compositional OODs composed of valid ID components. Inspired by recognition-by-components theory, we present a training-free Component-Based OOD Detection (CoOD) framework that addresses the existing limitations by decomposing inputs into functional components. To instantiate CoOD, we derive Component Shift Score (CSS) to detect local appearance shifts, and Compositional Consistency Score (CCS) to identify cross-component compositional inconsistencies. Empirically, CoOD achieves consistent improvements on both coarse- and fine-grained OOD detection.

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May 8, 2026 · cs.CVJ/K move · Enter open · S save

Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis

Boyang Dai, Chaoqi Chen, Yizhou Yu

1The University of Hong Kong · 2Shenzhen University · 3Shenzhen Loop Area Institute

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) patterns within images. The proposed method introduces a new OOD detection paradigm that understands object co-occurrence within an image by predicting disentangled representations for the test sample, then adaptively divides patterns into three scenarios based on object co-occurrence patterns observed in ID training data, and finally performs OOD detection in a divide-and-conquer manner. By doing so, OCO can distinguish near-OOD by considering the semantic contextual relationships present in their images, avoiding the tendency to focus solely on simple, easily learnable regions. We evaluate OCO through experiments across challenging and full-spectrum OOD settings, demonstrating competitive results and confirming its ability to address both semantic and covariate shifts. Code is released at https://github.com/Michael-McQueen/OCO.

Out-Of-Distribution Detection Out-Of-Distribution Open-Vocabulary Object Detection Diverse Occlusion-And-Revelation Scenarios Co-Occurrence Visual Perception Simplification

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Abstract

Explore similar work

Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis

MM++: Post-Hoc Scale-Invariant Multilayer OOD Detection via Top-K Gated Feature Fusion

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