Kavli Affiliate: Ke Wang
| First 5 Authors: Amirhossein Ansari, Amirhossein Ansari, , ,
| Summary:
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. Source code is
available at https://github.com/ah-ansari/NegRefine.
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