NegRefine: Refining Negative Label-Based Zero-Shot OOD Detection

Amirhossein Ansari; Ke Wang; Pulei Xiong

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arXiv · 2507.09795

NegRefine: Refining Negative Label-Based Zero-Shot OOD Detection

Amirhossein Ansari·Ke Wang·Pulei Xiong

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Abstract

Recent advancements in Vision-Language Models like CLIP have enabled zero-shot OOD detection by leveraging both image and textual label information. Among these, negative label-based methods such as NegLabel and CSP have shown promising results by utilizing a lexicon of words to define negative labels for distinguishing OOD samples. However, these methods suffer from detecting in-distribution samples as OOD due to negative labels that are subcategories of in-distribution labels or proper nouns. They also face limitations in handling images that match multiple in-distribution and negative labels. We propose NegRefine, a novel negative label refinement framework for zero-shot OOD detection. By introducing a filtering mechanism to exclude subcategory labels and proper nouns from the negative label set and incorporating a multi-matching-aware scoring function that dynamically adjusts the contributions of multiple labels matching an image, NegRefine ensures a more robust separation between in-distribution and OOD samples. We evaluate NegRefine on large-scale benchmarks, including ImageNet-1K. The code is available at https://github.com/ah-ansari/NegRefine.

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Amirhossein Ansari, Ke Wang, Pulei Xiong. 2025-07-13. NegRefine: Refining Negative Label-Based Zero-Shot OOD Detection. https://arxiv.org/abs/2507.09795

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