{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T07:10:37Z","timestamp":1778051437460,"version":"3.51.4"},"reference-count":75,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,6]]},"DOI":"10.1109\/wacv61042.2026.00199","type":"proceedings-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T19:59:32Z","timestamp":1778011172000},"page":"1999-2010","source":"Crossref","is-referenced-by-count":0,"title":["ClusterMine: Robust Label-Free Visual Out-Of-Distribution Detection via Concept Mining from Text Corpora"],"prefix":"10.1109","author":[{"given":"Nikolas","family":"Adaloglou","sequence":"first","affiliation":[{"name":"Heinrich Heine University of D&#x00FC;sseldorf"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Petrusheva","sequence":"additional","affiliation":[{"name":"Heinrich Heine University of D&#x00FC;sseldorf"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Asker","sequence":"additional","affiliation":[{"name":"Heinrich Heine University of D&#x00FC;sseldorf"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Michels","sequence":"additional","affiliation":[{"name":"Heinrich Heine University of D&#x00FC;sseldorf"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Kollmann","sequence":"additional","affiliation":[{"name":"Heinrich Heine University of D&#x00FC;sseldorf"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Adapting contrastive language-image pretrained (clip) models for out-of-distribution detection","author":"Adaloglou","year":"2023"},{"key":"ref2","article-title":"Exploring the limits of deep image clustering using pretrained models","author":"Adaloglou","year":"2023"},{"key":"ref3","article-title":"Exploring the limits of deep image clustering using pretrained models","volume-title":"34th British Machine Vision Conference 2023, BMVC 2023, Aberdeen, UK, November 20-24, 2023","author":"Adaloglou"},{"key":"ref4","article-title":"Rethinking cluster-conditioned diffusion models","author":"Adaloglou","year":"2024"},{"key":"ref5","article-title":"Scaling up deep clustering methods beyond imagenet-1k","author":"Adaloglou","year":"2024"},{"key":"ref6","first-page":"1454","article-title":"Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection","volume-title":"International Conference on Machine Learning","author":"Bai"},{"key":"ref7","author":"Betker","journal-title":"Improving image generation with better captions"},{"key":"ref8","article-title":"Are we done with imagenet?","author":"Beyer","year":"2020"},{"key":"ref9","author":"Bhatia","journal-title":"Part-of-speech tagging kaggle dataset"},{"key":"ref10","article-title":"In or out? fixing imagenet out-of-distribution detection evaluation","volume-title":"ICML","author":"Bitterwolf"},{"key":"ref11","first-page":"1392","article-title":"Unlabelled data improves bayesian uncertainty calibration under covariate shift","volume-title":"International conference on machine learning","author":"Chan"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00276"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref14","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"International Conference on Learning Representations","author":"Dosovitskiy"},{"key":"ref15","article-title":"Towards unknown-aware learning with virtual outlier synthesis","volume-title":"International Conference on Learning Representations","author":"Du"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20610"},{"key":"ref17","article-title":"Data filtering networks","author":"Fang","year":"2023"},{"key":"ref18","first-page":"7068","article-title":"Exploring the limits of out-of-distribution detection","volume":"34","author":"Fort","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/wacv61041.2025.00522"},{"key":"ref20","article-title":"A framework for benchmarking class-out-of-distribution detection and its application to imagenet","volume-title":"The Eleventh International Conference on Learning Representations","author":"Galil"},{"key":"ref21","article-title":"Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","volume-title":"International Conference on Learning Representations","author":"Geirhos"},{"key":"ref22","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"International conference on machine learning","author":"Guo"},{"key":"ref23","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","volume-title":"International Conference on Learning Representations","author":"Hendrycks"},{"key":"ref24","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"International Conference on Learning Representations","author":"Hendrycks"},{"key":"ref25","article-title":"Deep anomaly detection with outlier exposure","volume-title":"International Conference on Learning Representations","author":"Hendrycks"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"ref29","first-page":"8759","article-title":"Scaling out-of-distribution detection for real-world settings","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Hendrycks"},{"key":"ref30","article-title":"Is it a fruit, an apple or a granny smith? predicting the basic level in a concept hierarchy","author":"Hollink","year":"2019"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00860"},{"key":"ref32","article-title":"Openclip","author":"Ilharco","year":"2021","journal-title":"If you use this software, please cite it as below"},{"key":"ref33","first-page":"15067","article-title":"Detecting out-of-distribution data through in-distribution class prior","volume-title":"International Conference on Machine Learning","author":"Jiang"},{"key":"ref34","article-title":"Negative label guided OOD detection with pretrained vision-language models","volume-title":"The Twelfth International Conference on Learning Representations","author":"Jiang"},{"key":"ref35","article-title":"Scaling laws for neural language models","author":"Kaplan","year":"2020"},{"key":"ref36","first-page":"31","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","author":"Lee","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51701.2025.00428"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01665"},{"key":"ref39","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"International Conference on Learning Representations","author":"Liang"},{"key":"ref40","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ISEMANTIC.2018.8549751"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"ref45","article-title":"How does fine-tuning impact out-of-distribution detection for vision-language models?","author":"Ming","year":"2023"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2543"},{"key":"ref47","article-title":"Dinov2: Learning robust visual features without supervision","author":"Oquab","year":"2023"},{"key":"ref48","first-page":"32","article-title":"Can you trust your model\u2019s uncertainty? evaluating predictive uncertainty under dataset shift","author":"Ovadia","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref49","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"International Conference on Machine Learning","author":"Radford"},{"key":"ref50","first-page":"5389","article-title":"Do imagenet classifiers generalize to imagenet?","volume-title":"International conference on machine learning","author":"Recht"},{"key":"ref51","article-title":"A simple fix to mahalanobis distance for improving near-ood detection","author":"Ren","year":"2021"},{"key":"ref52","author":"Ren","year":"2021","journal-title":"A simple fix to mahalanobis distance for improving near-ood detection"},{"key":"ref53","article-title":"Imagenet-21k pretraining for the masses","author":"Ridnik","year":"2021"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0285(76)90013-X"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00088"},{"key":"ref56","first-page":"144","article-title":"React: Out-of-distribution detection with rectified activations","volume":"34","author":"Sun","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref57","first-page":"20827","article-title":"Out-of-distribution detection with deep nearest neighbors","volume-title":"International Conference on Machine Learning","author":"Sun"},{"key":"ref58","article-title":"Non-parametric outlier synthesis","volume-title":"The Eleventh International Conference on Learning Representations","author":"Tao"},{"key":"ref59","article-title":"Exploring covariate and concept shift for detection and calibration of out-ofdistribution data","author":"Tian","year":"2021"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_16"},{"key":"ref61","article-title":"Automatic data curation for self-supervised learning: A clustering-based approach","author":"Vo","year":"2024"},{"key":"ref62","first-page":"32","article-title":"Learning robust global representations by penalizing local predictive power","author":"Wang","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref63","first-page":"29074","article-title":"Can multi-label classification networks know what they don\u2019t know?","volume":"34","author":"Wang","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00173"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00917"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.5040\/9798881817916.ch-004"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2362"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01811-z"},{"key":"ref70","article-title":"Imagenet-ood: Deciphering modern out-of-distribution detection algorithms","author":"Yang","year":"2023"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01100"},{"key":"ref72","article-title":"Openood v1. 5: Enhanced benchmark for out-of-distribution detection","author":"Zhang","year":"2023"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2228"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1224"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00330"}],"event":{"name":"2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","location":"Tucson, AZ, USA","start":{"date-parts":[[2026,3,6]]},"end":{"date-parts":[[2026,3,10]]}},"container-title":["2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11491838\/11491925\/11492203.pdf?arnumber=11492203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:11:02Z","timestamp":1778047862000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11492203\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":75,"URL":"https:\/\/doi.org\/10.1109\/wacv61042.2026.00199","relation":{},"subject":[],"published":{"date-parts":[[2026,3,6]]}}}