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GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection

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arxiv 2304.04521 v4 pith:HP2RI6YE submitted 2023-04-10 cs.CV

Atsuyuki Miyai , Qing Yu , Go Irie , Kiyoharu Aizawa This is my paper
classification cs.CV
keywords gl-mcmimagesdetectionlocalmethodstypezero-shotconcept
verification ladder T0 review T1 audit T2 compute T3 formal
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Zero-shot out-of-distribution (OOD) detection is a task that detects OOD images during inference with only in-distribution (ID) class names. Existing methods assume ID images contain a single, centered object, and do not consider the more realistic multi-object scenarios, where both ID and OOD objects are present. To meet the needs of many users, the detection method must have the flexibility to adapt the type of ID images. To this end, we present Global-Local Maximum Concept Matching (GL-MCM), which incorporates local image scores as an auxiliary score to enhance the separability of global and local visual features. Due to the simple ensemble score function design, GL-MCM can control the type of ID images with a single weight parameter. Experiments on ImageNet and multi-object benchmarks demonstrate that GL-MCM outperforms baseline zero-shot methods and is comparable to fully supervised methods. Furthermore, GL-MCM offers strong flexibility in adjusting the target type of ID images. The code is available via https://github.com/AtsuMiyai/GL-MCM.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Models

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    NegAS uses negative labels for attention guidance and sigmoid scoring to improve OOD detection in VLM-based object detectors while preserving ID performance.

  2. Component-Based Out-of-Distribution Detection

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    CoOD decomposes inputs into components and applies Component Shift Score plus Compositional Consistency Score to improve detection of both standard and compositional out-of-distribution data.

  3. $\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization

    cs.CV 2025-10 reject novelty 5.0 of 10

    ΔEnergy, an energy-change OOD score for CLIP, and its EBM fine-tuning loss simultaneously improve OOD detection and covariate-shift generalization.

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