[{"date_created":"2026-01-29T14:26:47Z","OA_type":"green","publication":"39th Annual Conference on Neural Information Processing Systems","status":"public","arxiv":1,"ddc":["000"],"related_material":{"link":[{"url":"https://github.com/berkerdemirel/ORA-OOD-Detection-with-Relative-Angles","relation":"software"}]},"alternative_title":["Advances in Neural Information Processing Systems"],"language":[{"iso":"eng"}],"month":"12","oa":1,"date_updated":"2026-02-16T11:38:25Z","acknowledgement":"This research was funded in whole or in part by the Austrian Science Fund (FWF) 10.55776/COE12. For open access purposes, the author has applied a CC BY public copyright license to any accepted manuscript version arising from this submission.\r\n","department":[{"_id":"FrLo"}],"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"day":"01","year":"2025","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"epub_ahead","type":"conference","OA_place":"repository","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2410.04525","open_access":"1"}],"has_accepted_license":"1","author":[{"full_name":"Demirel, Berker","id":"8b4bc47f-3200-11ee-973b-8f0e7be21a9f","last_name":"Demirel","first_name":"Berker"},{"first_name":"Marco ","full_name":"Fumero, Marco ","last_name":"Fumero"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","orcid":"0000-0002-4850-0683","first_name":"Francesco"}],"conference":{"end_date":"2025-12-07","location":"San Diego, CA, United States","name":"NeurIPS: Neural Information Processing Systems","start_date":"2025-12-02"},"title":"Out-of-Distribution detection with relative angles","abstract":[{"lang":"eng","text":"Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable model should ideally abstain from making decisions in this out-of-distribution (OOD) setting. Existing state-of-the-art methods primarily focus on feature distances, such as k-th nearest neighbors and distances to decision boundaries, either overlooking or ineffectively using in-distribution statistics. In this work, we propose a novel angle-based metric for OOD detection that is computed relative to the in-distribution structure. We demonstrate that the angles between feature representations and decision boundaries, viewed from the mean of in-distribution features, serve as an effective discriminative factor between ID and OOD data. We evaluate our method on nine ImageNet-pretrained models. Our approach achieves the lowest FPR in 5 out of 9 ImageNet models, obtains the best average FPR overall, and consistently ranking among the top 3 across all evaluated models. Furthermore, we highlight the benefits of contrastive representations by showing strong performance with ResNet SCL and CLIP architectures. Finally, we demonstrate that the scale-invariant nature of our score enables an ensemble strategy via simple score summation. "}],"corr_author":"1","quality_controlled":"1","external_id":{"arxiv":["2410.04525"]},"article_processing_charge":"No","volume":38,"_id":"21070","publisher":"Neural Information Processing Systems Foundation","publication_identifier":{"issn":["1049-5258"]},"citation":{"mla":"Demirel, Berker, et al. “Out-of-Distribution Detection with Relative Angles.” <i>39th Annual Conference on Neural Information Processing Systems</i>, vol. 38, Neural Information Processing Systems Foundation, 2025.","short":"B. Demirel, M. Fumero, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.","chicago":"Demirel, Berker, Marco  Fumero, and Francesco Locatello. “Out-of-Distribution Detection with Relative Angles.” In <i>39th Annual Conference on Neural Information Processing Systems</i>, Vol. 38. Neural Information Processing Systems Foundation, 2025.","ieee":"B. Demirel, M. Fumero, and F. Locatello, “Out-of-Distribution detection with relative angles,” in <i>39th Annual Conference on Neural Information Processing Systems</i>, San Diego, CA, United States, 2025, vol. 38.","ista":"Demirel B, Fumero M, Locatello F. 2025. Out-of-Distribution detection with relative angles. 39th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 38.","ama":"Demirel B, Fumero M, Locatello F. Out-of-Distribution detection with relative angles. In: <i>39th Annual Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information Processing Systems Foundation; 2025.","apa":"Demirel, B., Fumero, M., &#38; Locatello, F. (2025). Out-of-Distribution detection with relative angles. In <i>39th Annual Conference on Neural Information Processing Systems</i> (Vol. 38). San Diego, CA, United States: Neural Information Processing Systems Foundation."},"date_published":"2025-12-01T00:00:00Z","oa_version":"Preprint","intvolume":"        38"},{"_id":"18956","publisher":"IEEE","citation":{"ista":"Demirel B, Ozkan H. 2024. Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. 2024 IEEE International Conference on Image Processing. ICIP: International Conference on Image Processing, 977–983.","ama":"Demirel B, Ozkan H. Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. In: <i>2024 IEEE International Conference on Image Processing</i>. IEEE; 2024:977-983. doi:<a href=\"https://doi.org/10.1109/icip51287.2024.10647499\">10.1109/icip51287.2024.10647499</a>","apa":"Demirel, B., &#38; Ozkan, H. (2024). Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. In <i>2024 IEEE International Conference on Image Processing</i> (pp. 977–983). Abu Dhabi, United Arab Emirates: IEEE. <a href=\"https://doi.org/10.1109/icip51287.2024.10647499\">https://doi.org/10.1109/icip51287.2024.10647499</a>","mla":"Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image.” <i>2024 IEEE International Conference on Image Processing</i>, IEEE, 2024, pp. 977–83, doi:<a href=\"https://doi.org/10.1109/icip51287.2024.10647499\">10.1109/icip51287.2024.10647499</a>.","ieee":"B. Demirel and H. Ozkan, “Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image,” in <i>2024 IEEE International Conference on Image Processing</i>, Abu Dhabi, United Arab Emirates, 2024, pp. 977–983.","chicago":"Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image.” In <i>2024 IEEE International Conference on Image Processing</i>, 977–83. IEEE, 2024. <a href=\"https://doi.org/10.1109/icip51287.2024.10647499\">https://doi.org/10.1109/icip51287.2024.10647499</a>.","short":"B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing, IEEE, 2024, pp. 977–983."},"date_published":"2024-11-01T00:00:00Z","publication_identifier":{"eissn":["2381-8549"],"eisbn":["9798350349399"]},"article_processing_charge":"No","oa_version":"Preprint","page":"977-983","conference":{"location":"Abu Dhabi, United Arab Emirates","start_date":"2024-10-27","name":"ICIP: International Conference on Image Processing","end_date":"2024-10-30"},"title":"Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image","abstract":[{"lang":"eng","text":"Group Activity Recognition (GAR) aims to detect the activity performed by multiple actors in a scene. Prior works model the spatio-temporal features based on the RGB, optical flow or keypoint data types. On the contrary, our hypothesis is that by only using the RGB data without temporality, the performance can be maintained with a negligible loss in accuracy. To that end, we propose a novel GAR technique for volleyball videos, DECOMPL, which consists of two complementary branches. In the visual branch, it extracts the features using attention pooling. In the coordinate branch, it considers the configuration of the players and extracts the spatial information from the box coordinates. Moreover, we analyzed the Volleyball dataset that the recent literature is mostly based on, and systematically reannotated it to emphasize the group concept. Experimental results demonstrated the effectiveness of the proposed model DECOMPL, which delivered the best/second best GAR performance with the reannotations/original annotations among the comparable state-of-the-art methods. Code and new annotations are available at GitHub: https://github.com/berkerdemirel/decompl"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2303.06439"}],"doi":"10.1109/icip51287.2024.10647499","author":[{"full_name":"Demirel, Berker","last_name":"Demirel","id":"8b4bc47f-3200-11ee-973b-8f0e7be21a9f","first_name":"Berker"},{"full_name":"Ozkan, Huseyin","last_name":"Ozkan","first_name":"Huseyin"}],"external_id":{"isi":["001442947000143"],"arxiv":["2303.06439"]},"corr_author":"1","quality_controlled":"1","department":[{"_id":"FrLo"}],"isi":1,"month":"11","language":[{"iso":"eng"}],"date_updated":"2025-09-09T12:13:12Z","oa":1,"OA_place":"repository","day":"01","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","year":"2024","publication_status":"published","type":"conference","related_material":{"link":[{"url":"https://github.com/berkerdemirel/decompl","relation":"software"}]},"arxiv":1,"date_created":"2025-01-29T12:22:24Z","OA_type":"green","publication":"2024 IEEE International Conference on Image Processing","status":"public"}]