{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:19:51Z","timestamp":1771953591692,"version":"3.50.1"},"reference-count":56,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"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":[[2024,5,13]]},"DOI":"10.1109\/icra57147.2024.10611342","type":"proceedings-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T17:51:05Z","timestamp":1723139465000},"page":"6943-6951","source":"Crossref","is-referenced-by-count":9,"title":["Deep Evidential Uncertainty Estimation for Semantic Segmentation under Out-Of-Distribution Obstacles"],"prefix":"10.1109","author":[{"given":"Siddharth","family":"Ancha","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology,Computer Science and Artifical Intelligence Lab (CSAIL),Cambridge,MA,USA,02139"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip R.","family":"Osteen","sequence":"additional","affiliation":[{"name":"DEVCOM Army Research Laboratory,Adelphi,MD,USA,20783"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Roy","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology,Computer Science and Artifical Intelligence Lab (CSAIL),Cambridge,MA,USA,02139"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"14927","article-title":"Deep evidential regression","volume":"33","author":"Amini","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/2.30717"},{"key":"ref3","first-page":"573","article-title":"Invertible residual networks","volume-title":"International Conference on Machine Learning (ICML)","author":"Behrmann"},{"key":"ref4","first-page":"1613","article-title":"Weight uncertainty in neural network","volume-title":"International Conference on Machine Learning (ICML)","author":"Blundell"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2"},{"key":"ref6","first-page":"1356","article-title":"Posterior network: Uncertainty estimation without OOD samples via density-based pseudo-counts","volume":"33","author":"Charpentier","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref7","article-title":"Natural posterior network: Deep Bayesian predictive uncertainty for exponential family distributions","volume-title":"International Conference on Learning Representations","author":"Charpentier"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref9","article-title":"Residual flows for invertible generative modeling","volume":"32","author":"Chen","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref10","article-title":"WAIC, but why? Generative ensembles for robust anomaly detection","author":"Choi","year":"2018"},{"key":"ref11","first-page":"3213","article-title":"The Cityscapes dataset for semantic urban scene understanding","volume-title":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition","author":"Cordts"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2307\/2987588.JSTOR:2987588"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.2307\/2984875"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2021.xvii.021"},{"key":"ref15","article-title":"Topology-matching normalizing flows for out-of-distribution detection in robot learning","volume-title":"7th Annual Conference on Robot Learning (CoRL)","author":"Feng"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9982190"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.iatssr.2019.11.008"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1506.02142"},{"key":"ref19","article-title":"Uncertainty in deep learning","volume-title":"Ph.D. Thesis","author":"Gal","year":"2016"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3187278"},{"key":"ref23","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"International Conference on Machine Learning (ICML)","author":"Guo"},{"key":"ref24","first-page":"1861","article-title":"Probabilistic backpropagation for scalable learning of bayesian neural networks","volume-title":"International Conference on Machine Learning (ICML)","author":"Hern\u00e1ndez-Lobato"},{"key":"ref25","first-page":"4615","article-title":"Semi-supervised learning with normalizing flows","volume-title":"International Conference on Machine Learning (ICML)","author":"Izmailov"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20080-9_10"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/10991459_51"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref29","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"ref30","first-page":"20578","article-title":"Why normalizing flows fail to detect out-of-distribution data","volume":"33","author":"Kirichenko","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"ref32","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan","year":"2017","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref34","article-title":"A simple baseline for bayesian uncertainty in deep learning","volume":"32","author":"Maddox","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref35","article-title":"Predictive uncertainty estimation via prior networks","volume":"31","author":"Malinin","year":"2018","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref36","article-title":"Reverse KL-divergence training of prior networks: Improved uncertainty and adversarial robustness","volume":"32","author":"Malinin","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref37","article-title":"Regression prior networks","author":"Malinin","year":"2020"},{"key":"ref38","article-title":"Ensemble distribution distillation","volume-title":"International Conference on Learning Representations","author":"Malinin"},{"key":"ref39","first-page":"153","article-title":"Autonomous navigation system for planetary exploration rover based on artificial potential fields","volume-title":"Proceedings of Dynamics and Control of Systems and Structures in Space (DCSSS) 6th Conference","author":"Massari"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2"},{"key":"ref41","volume-title":"Probabilistic Machine Learning: Advanced Topics","author":"Murphy","year":"2023"},{"key":"ref42","article-title":"Do deep generative models know what they don\u2019t know?","volume-title":"7th International Conference on Learning Representations, (ICLR)","author":"Nalisnick"},{"key":"ref43","article-title":"Can you trust your model\u2019s uncertainty? evaluating predictive uncertainty under dataset shift","volume":"32","author":"Ovadia","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"issue":"1","key":"ref44","first-page":"2617","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios","year":"2021","journal-title":"The Journal of Machine Learning Research (JMLR)"},{"key":"ref45","first-page":"1530","article-title":"Variational inference with normalizing flows","volume-title":"International Conference on Machine Learning (ICML)","author":"Rezende"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2017.xiii.064"},{"key":"ref47","article-title":"Evidential deep learning to quantify classification uncertainty","volume":"31","author":"Sensoy","year":"2018","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref48","first-page":"4915","article-title":"Resampling base distributions of normalizing flows","volume-title":"International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Stimper"},{"key":"ref49","article-title":"Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation","author":"Ulmer","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50115-4_41"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989540"},{"key":"ref52","first-page":"681","article-title":"Bayesian learning via stochastic gradient Langevin dynamics","volume-title":"Proceedings of the 28th International Conference on Machine Learning (ICML-11)","author":"Welling"},{"key":"ref53","first-page":"5000","article-title":"A RUGD dataset for autonomous navigation and visual perception in unstructured outdoor environments","volume-title":"2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","author":"Wigness"},{"key":"ref54","article-title":"Dirichlet distribution: Wikipedia, The Free Encyclopedia","year":"2023"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2983149"},{"key":"ref56","first-page":"12427","article-title":"Understanding failures in out-of-distribution detection with deep generative models","volume-title":"International Conference on Machine Learning (ICLR)","author":"Zhang"}],"event":{"name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","location":"Yokohama, Japan","start":{"date-parts":[[2024,5,13]]},"end":{"date-parts":[[2024,5,17]]}},"container-title":["2024 IEEE International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10609961\/10609862\/10611342.pdf?arnumber=10611342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T04:16:48Z","timestamp":1723349808000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10611342\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/icra57147.2024.10611342","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]}}}