{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:40:03Z","timestamp":1783968003463,"version":"3.55.0"},"reference-count":66,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"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":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.02195","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"23646-23656","source":"Crossref","is-referenced-by-count":1,"title":["Prior2former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation"],"prefix":"10.1109","author":[{"given":"Sebastian","family":"Schmidt","sequence":"first","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"K\u00f6rner","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Fuchsgruber","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Gasperini","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Tombari","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"G\u00fcnnemann","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Maskomaly: Zero-shot mask anomaly segmentation","volume-title":"Proceedings of the British Machine Vision Conference (BMVC)","author":"Ackermann"},{"key":"ref2","article-title":"Uncertainty estimation using a single deep deterministic neural network","volume-title":"Proceedings of the Internation Conference on Machine Learning (ICML)","author":"Van Amersfoort"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01511-6"},{"key":"ref4","article-title":"Segmentmeifyoucan: A benchmark for anomaly segmentation","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track","author":"Chan"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00508"},{"key":"ref6","article-title":"Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Charpentier"},{"key":"ref7","article-title":"Natural posterior network: Deep bayesian uncertainty for exponential family distributions","volume-title":"Proceedings of the Internation Conference on Machine Learning (ICML)","author":"Charpentier"},{"key":"ref8","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"Chen","year":"2017","journal-title":"Arxiv"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1107\/978-3-030-01234-2_49"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"ref11","article-title":"Per-pixel classification is not all you need for semantic segmentation","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Cheng"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01506"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref15","article-title":"Outlier detection by ensembling uncertainty with negative objectness","volume-title":"Proceedings of the British Machine Vision Conference (BMVC)","author":"Deli\u0107"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1968.tb00722.x"},{"key":"ref17","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume-title":"Proceedings of the Second International Conference on Knowledge Discovery and Data Mining","author":"Ester"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1506.02142"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3130976"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01770"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10562-9"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00295"},{"key":"ref23","article-title":"On calibration of modern neural networks","volume-title":"Proceedings of the Internation Conference on Machine Learning (ICML)","author":"Guo"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00013"},{"key":"ref26","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"Proceedings of the Internation Conference on Learning and Representation (ICLR)","author":"Hendrycks"},{"key":"ref27","article-title":"Scaling out-of-distribution detection for realworld settings","volume-title":"Proceedings of the Internation Conference on Machine Learning (ICML)","author":"Hendrycks"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00123"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01514"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00963"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref33","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Lakshminarayanan"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2274"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref36","article-title":"Simple and principled uncertainty estimation with deterministic deep learning via distance awareness","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Liu"},{"key":"ref37","article-title":"Energy-based out-of-distribution detection","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Liu"},{"key":"ref38","article-title":"Decoupled weight decay regularization","volume-title":"Proceedings of the Internation Conference on Learning and Representation (ICLR)","author":"Loshchilov"},{"key":"ref39","article-title":"Predictive uncertainty estimation via prior networks","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Malinin"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02336"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00072"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-54605-1_4"},{"key":"ref43","article-title":"Oodis: Anomaly instance segmentation benchmark","author":"Nekrasov","year":"2024","journal-title":"Arvix"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759186"},{"key":"ref45","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proceedings of the Internation Conference on Machine Learning (ICML)","author":"Radford"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.00373"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref48","article-title":"Second-order uncertainty quantification: A distance-based approach","volume-title":"Proceedings of the Internation Conference on Machine Learning (ICML)","author":"Sale"},{"key":"ref49","article-title":"Stream-based active learning by exploiting temporal properties in perception with temporal predicted loss","volume-title":"Proceedings of the British Machine Vision Conference (BMVC)","author":"Schmidt"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304565"},{"key":"ref51","article-title":"A unified approach towards active learning and out-of-distribution detection","author":"Schmidt","year":"2024","journal-title":"Arxiv"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10802242"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02391"},{"key":"ref54","article-title":"Evidential deep learning to quantify classification uncertainty","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Sensoy"},{"key":"ref55","article-title":"Open-World Panoptic Segmentation","author":"Sodano","year":"2024","journal-title":"Arvix"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00307"},{"key":"ref57","article-title":"Graph posterior network: Bayesian predictive uncertainty for node classification","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Stadler"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19842-7_15"},{"key":"ref60","first-page":"384","article-title":"Identifying unknown instances for autonomous driving","volume-title":"Proceedings of the Conference on Robot Learning (CoRL)","author":"Wong"},{"key":"ref61","article-title":"GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zach"},{"key":"ref62","article-title":"Segformer: Simple and efficient design for semantic segmentation with transformers","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS)","author":"Xie"},{"key":"ref63","article-title":"Dual decision improves open-set panoptic segmentation","volume-title":"Proceedings of the British Machine Vision Conference (BMVC)","author":"Xu"},{"key":"ref64","article-title":"Openood: Benchmarking generalized out-of-distribution detection","volume-title":"Proceedings of Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track","author":"Yang"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28535"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11446191.pdf?arnumber=11446191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:20:50Z","timestamp":1777612850000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11446191\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":66,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.02195","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}