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

IF 11.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Computer Vision Pub Date : 2025-01-22 DOI:10.1007/s11263-025-02356-z

Atsuyuki Miyai, Qing Yu, Go Irie, Kiyoharu Aizawa

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Abstract

Zero-shot 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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GL-MCM：零弹偏离检测的全局和局部极大值概念匹配

零镜头 OOD 检测是一项在推理过程中检测只有分布（ID）类名的 OOD 图像的任务。现有方法假定 ID 图像包含单个居中对象，而不考虑更现实的多对象情况，即同时存在 ID 和 OOD 对象。为了满足众多用户的需求，检测方法必须具有适应 ID 图像类型的灵活性。为此，我们提出了全局-局部最大概念匹配法（GL-MCM），它将局部图像得分作为辅助得分，以增强全局和局部视觉特征的可分离性。由于采用了简单的集合得分函数设计，GL-MCM 只需一个权重参数就能控制 ID 图像的类型。在 ImageNet 和多对象基准上进行的实验表明，GL-MCM 的性能优于基线零拍摄方法，并可与完全监督方法相媲美。此外，GL-MCM 在调整 ID 图像的目标类型方面具有很强的灵活性。代码可通过 https://github.com/AtsuMiyai/GL-MCM 获取。

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来源期刊

International Journal of Computer Vision 工程技术-计算机：人工智能

CiteScore

29.80

自引率

2.10%

发文量

163

审稿时长

6 months

期刊介绍： The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.