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

• URL: http://arxiv.org/abs/2507.09795v2
• Date: Sun, 20 Jul 2025 03:09:19 GMT
• Title: NegRefine: Refining Negative Label-Based Zero-Shot OOD Detection
• Abstract summary: NegRefine is a novel negative label refinement framework for zero-shot OOD detection.<n>We introduce a filtering mechanism to exclude subcategory labels and proper nouns from the negative label set.<n>We evaluate NegRefine on large-scale benchmarks, including ImageNet-1K.
• Score: 3.9940875711185124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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