Paper
Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs
Authors
Zhikang Xu, Qianqian Xu, Zitai Wang, Cong Hua, Sicong Li, Zhiyong Yang, Qingming Huang
Abstract
Out-of-distribution (OOD) detection seeks to identify samples from unknown classes, a critical capability for deploying machine learning models in open-world scenarios. Recent research has demonstrated that Vision-Language Models (VLMs) can effectively leverage their multi-modal representations for OOD detection. However, current methods often incorporate intra-modal distance during OOD detection, such as comparing negative texts with ID labels or comparing test images with image proxies. This design paradigm creates an inherent inconsistency against the inter-modal distance that CLIP-like VLMs are optimized for, potentially leading to suboptimal performance. To address this limitation, we propose InterNeg, a simple yet effective framework that systematically utilizes consistent inter-modal distance enhancement from textual and visual perspectives. From the textual perspective, we devise an inter-modal criterion for selecting negative texts. From the visual perspective, we dynamically identify high-confidence OOD images and invert them into the textual space, generating extra negative text embeddings guided by inter-modal distance. Extensive experiments across multiple benchmarks demonstrate the superiority of our approach. Notably, our InterNeg achieves state-of-the-art performance compared to existing works, with a 3.47\% reduction in FPR95 on the large-scale ImageNet benchmark and a 5.50\% improvement in AUROC on the challenging Near-OOD benchmark.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.02618v1</id>\n <title>Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs</title>\n <updated>2026-03-03T05:44:47Z</updated>\n <link href='https://arxiv.org/abs/2603.02618v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.02618v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Out-of-distribution (OOD) detection seeks to identify samples from unknown classes, a critical capability for deploying machine learning models in open-world scenarios. Recent research has demonstrated that Vision-Language Models (VLMs) can effectively leverage their multi-modal representations for OOD detection. However, current methods often incorporate intra-modal distance during OOD detection, such as comparing negative texts with ID labels or comparing test images with image proxies. This design paradigm creates an inherent inconsistency against the inter-modal distance that CLIP-like VLMs are optimized for, potentially leading to suboptimal performance. To address this limitation, we propose InterNeg, a simple yet effective framework that systematically utilizes consistent inter-modal distance enhancement from textual and visual perspectives. From the textual perspective, we devise an inter-modal criterion for selecting negative texts. From the visual perspective, we dynamically identify high-confidence OOD images and invert them into the textual space, generating extra negative text embeddings guided by inter-modal distance. Extensive experiments across multiple benchmarks demonstrate the superiority of our approach. Notably, our InterNeg achieves state-of-the-art performance compared to existing works, with a 3.47\\% reduction in FPR95 on the large-scale ImageNet benchmark and a 5.50\\% improvement in AUROC on the challenging Near-OOD benchmark.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-03T05:44:47Z</published>\n <arxiv:comment>Accepted by the main track of CVPR 2026</arxiv:comment>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Zhikang Xu</name>\n </author>\n <author>\n <name>Qianqian Xu</name>\n </author>\n <author>\n <name>Zitai Wang</name>\n </author>\n <author>\n <name>Cong Hua</name>\n </author>\n <author>\n <name>Sicong Li</name>\n </author>\n <author>\n <name>Zhiyong Yang</name>\n </author>\n <author>\n <name>Qingming Huang</name>\n </author>\n </entry>"
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