Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs
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| Format: | Preprint |
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2026
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| _version_ | 1866918452889911296 |
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| author | Xu, Zhikang Xu, Qianqian Wang, Zitai Hua, Cong Li, Sicong Yang, Zhiyong Huang, Qingming |
| author_facet | Xu, Zhikang Xu, Qianqian Wang, Zitai Hua, Cong Li, Sicong Yang, Zhiyong Huang, Qingming |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_02618 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs Xu, Zhikang Xu, Qianqian Wang, Zitai Hua, Cong Li, Sicong Yang, Zhiyong Huang, Qingming Computer Vision and Pattern Recognition 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. |
| title | Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.02618 |