Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score

[2405.16091] Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2405.16091 (cs) [Submitted on 25 May 2024 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score Authors: Myong Chol Jung , Joanna Dipnall , Belinda Gabbe , He Zhao View a PDF of the paper titled Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score, by Myong Chol Jung and 3 other authors View PDF HTML (experimental) Abstract: Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explored generalisation abilities of these models in few-shot classification tasks and a few studies have addressed far out-of-distribution (OOD) of the models, their potential for addressing near OOD detection remains underexplored. Existing methods either require training from scratch, need fine-tuning, or are not designed for vision-language prompt learning. To address this, we introduce the Contrastive Logit Score (CLS), a novel post-hoc, plug-and-play scoring function. CLS significantly improves near OOD detection of pre-trained vision-language prompt learning methods without modifying their model architectures or requiring retraining. Our method achieves up to an 11.67% improvement in AUROC for near OOD detection with minimal computational overhead. Extensive evaluations validate the effectiveness, efficiency, and generalisability of our approach. Our code is available at this https URL . Comments: Published at International Journal of Computer Vision (IJCV) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2405.16091 [cs.CV] (or arXiv:2405.16091v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2405.16091 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Myong Chol Jung [ view email ] [v1] Sat, 25 May 2024 06:46:16 UTC (3,145 KB) [v2] Sat, 11 Apr 2026 11:14:46 UTC (2,277 KB) Full-text links: Access Paper: View a PDF of the paper titled Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score, by Myong Chol Jung and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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