{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T09:10:10Z","timestamp":1748250610659,"version":"3.41.0"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031915840","type":"print"},{"value":"9783031915857","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-91585-7_25","type":"book-chapter","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T08:30:20Z","timestamp":1748248220000},"page":"417-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Multi-Manifold Embedding for\u00a0Out-of-Distribution Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9261-1524","authenticated-orcid":false,"given":"Jeng-Lin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9325-5341","authenticated-orcid":false,"given":"Ming-Ching","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6165-7706","authenticated-orcid":false,"given":"Wei-Chao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"25_CR1","unstructured":"Basart, S., Mantas, M., Mohammadreza, M., Jacob, S., Dawn, S.: Scaling out-of-distribution detection for real-world settings. In: International Conference on Machine Learning (2022)"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Chan, R., Rottmann, M., Gottschalk, H.: Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In: ICCV, pp. 5128\u20135137 (2021)","DOI":"10.1109\/ICCV48922.2021.00508"},{"key":"25_CR3","doi-asserted-by":"publisher","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: CVPR, pp. 3606\u20133613 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.461","DOI":"10.1109\/CVPR.2014.461"},{"key":"25_CR4","unstructured":"Djurisic, A., Bozanic, N., Ashok, A., Liu, R.: Extremely simple activation shaping for out-of-distribution detection. In: ICLR (2022)"},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Ermolov, A., Mirvakhabova, L., Khrulkov, V., Sebe, N., Oseledets, I.: Hyperbolic vision transformers: combining improvements in metric learning. In: CVPR, pp. 7399\u20137409 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00726","DOI":"10.1109\/CVPR52688.2022.00726"},{"key":"25_CR6","first-page":"51","volume":"60","author":"X Fan","year":"2019","unstructured":"Fan, X., Jiang, W., Luo, H., Fei, M.: Spherereid: deep hypersphere manifold embedding for person re-identification. JVCIR 60, 51\u201358 (2019)","journal-title":"JVCIR"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Gao, Z., Wu, Y., Jia, Y., Harandi, M.: Curvature generation in curved spaces for few-shot learning. In: ICCV, pp. 8691\u20138700 (2021)","DOI":"10.1109\/ICCV48922.2021.00857"},{"key":"25_CR8","unstructured":"Ghadimi Atigh, M., Keller-Ressel, M., Mettes, P.: Hyperbolic busemann learning with ideal prototypes. In: NeurIPS, vol. 34, pp. 103\u2013115 (2021)"},{"key":"25_CR9","unstructured":"Gu, A., Sala, F., Gunel, B., R\u00e9, C.: Learning mixed-curvature representations in product spaces. In: ICLR (2019)"},{"key":"25_CR10","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2016)"},{"issue":"9","key":"25_CR11","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1016\/j.patcog.2015.04.003","volume":"48","author":"R Hettiarachchi","year":"2015","unstructured":"Hettiarachchi, R., Peters, J.F.: Multi-manifold LLE learning in pattern recognition. Pattern Recogn. 48(9), 2947\u20132960 (2015)","journal-title":"Pattern Recogn."},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Hsu, Y.C., Shen, Y., Jin, H., Kira, Z.: Generalized ODIN: detecting out-of-distribution image without learning from out-of-distribution data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10951\u201310960 (2020)","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"25_CR13","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data (2019)"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Khrulkov, V., Mirvakhabova, L., Ustinova, E., Oseledets, I., Lempitsky, V.: Hyperbolic image embeddings. In: CVPR, pp. 6418\u20136428 (2020)","DOI":"10.1109\/CVPR42600.2020.00645"},{"key":"25_CR15","unstructured":"Kim, H., Kim, K.: Spherization layer: representation using only angles. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) NeurIPS (2022)"},{"key":"25_CR16","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"25_CR17","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: NeurIPS, vol. 31 (2018)"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Li, J.L., Lee, C.C.: An enroll-to-verify approach for cross-task unseen emotion class recognition. IEEE Trans. Affect. Comput. 1\u201313 (2022). https:\/\/doi.org\/10.1109\/TAFFC.2022.3183166","DOI":"10.1109\/TAFFC.2022.3183166"},{"key":"25_CR19","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: ICLR (2018)"},{"key":"25_CR20","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. In: NeurIPS, vol. 33, pp. 21464\u201321475 (2020)"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Raj, B., Singh, R., Weller, A.: Sphereface revived: unifying hyperspherical face recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3159732"},{"key":"25_CR22","unstructured":"Ming, Y., Sun, Y., Dia, O., Li, Y.: How to exploit hyperspherical embeddings for out-of-distribution detection? In: ICLR (2023)"},{"key":"25_CR23","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"25_CR24","unstructured":"Pang, T., Yang, X., Dong, Y., Xu, K., Zhu, J., Su, H.: Boosting adversarial training with hypersphere embedding. In: NeurIPS, vol. 33, pp. 7779\u20137792 (2020)"},{"issue":"3","key":"25_CR25","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"25_CR26","unstructured":"Sehwag, V., Chiang, M., Mittal, P.: SSD: a unified framework for self-supervised outlier detection. In: ICLR (2020)"},{"key":"25_CR27","unstructured":"Shen, Z., et al.: Towards out-of-distribution generalization: a survey. arXiv preprint arXiv:2108.13624 (2021)"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Sun, Y., Li, Y.: DICE: leveraging sparsification for out-of-distribution detection. In: European Conference on Computer Vision, pp. 691\u2013708. Springer (2022)","DOI":"10.1007\/978-3-031-20053-3_40"},{"key":"25_CR29","unstructured":"Sun, Y., Ming, Y., Zhu, X., Li, Y.: Out-of-distribution detection with deep nearest neighbors. In: ICML, pp. 20827\u201320840. PMLR (2022)"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Van\u00a0Horn, G., et al.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8769\u20138778 (2018)","DOI":"10.1109\/CVPR.2018.00914"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Z., Feng, L., Zhang, W.: ViM: out-of-distribution with virtual-logit matching. In: CVPR, pp. 4921\u20134930 (2022)","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"25_CR32","unstructured":"Wei, H., Xie, R., Cheng, H., Feng, L., An, B., Li, Y.: Mitigating neural network overconfidence with logit normalization. In: ICML, pp. 23631\u201323644. PMLR (2022)"},{"key":"25_CR33","unstructured":"Wen, Y., Liu, W., Weller, A., Raj, B., Singh, R.: SphereFace2: binary classification is all you need for deep face recognition. In: ICLR (2022)"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Wu, B., et al.: Towards in-distribution compatible out-of-distribution detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 10333\u201310341 (2023)","DOI":"10.1609\/aaai.v37i9.26230"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3485\u20133492. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"25_CR36","unstructured":"Xu, P., Ehinger, K.A., Zhang, Y., Finkelstein, A., Kulkarni, S.R., Xiao, J.: TurkerGaze: crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755 (2015)"},{"key":"25_CR37","unstructured":"Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. arXiv preprint arXiv:2110.11334 (2021)"},{"key":"25_CR38","unstructured":"Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)"},{"issue":"6","key":"25_CR39","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2018","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452\u20131464 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2723009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91585-7_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T08:30:43Z","timestamp":1748248243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91585-7_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031915840","9783031915857"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91585-7_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}