{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:04:21Z","timestamp":1785305061329,"version":"3.55.0"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11263-026-02926-9","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:50:05Z","timestamp":1784051405000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GradAlign: Detecting Out-of-Distribution Samples via Gradient Concentration"],"prefix":"10.1007","volume":"134","author":[{"given":"Jiawei","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanpeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5341-5985","authenticated-orcid":false,"given":"Zechao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"5","key":"2926_CR1","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1007\/s11263-024-02234-0","volume":"133","author":"G Allabadi","year":"2025","unstructured":"Allabadi, G., Lucic, A., Wang, Y. X., & Adve, V. (2025). Learning to detect novel species with sam in the wild. International Journal of Computer Vision, 133(5), 2247\u20132258.","journal-title":"International Journal of Computer Vision"},{"issue":"2","key":"2926_CR2","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1162\/089976698300017746","volume":"10","author":"SI Amari","year":"1998","unstructured":"Amari, S. I. (1998). Natural gradient works efficiently in learning. Neural computation, 10(2), 251\u2013276.","journal-title":"Neural computation"},{"key":"2926_CR3","unstructured":"Basart, S., Mantas, M., Mohammadreza, M., Jacob, S., & Dawn, S. (2022). Scaling out-of-distribution detection for real-world settings. International Conference on Machine Learning"},{"key":"2926_CR4","unstructured":"Bitterwolf, J., Mueller, M., & Hein, M. (2023). In or out? fixing imagenet out-of-distribution detection evaluation. In: ICML. https:\/\/proceedings.mlr.press\/v202\/bitterwolf23a.html."},{"issue":"8","key":"2926_CR5","doi-asserted-by":"publisher","first-page":"3191","DOI":"10.1007\/s11263-024-02012-y","volume":"132","author":"H Chi","year":"2024","unstructured":"Chi, H., Yang, W., Liu, F., Lan, L., Qin, T., & Han, B. (2024). Does confusion really hurt novel class discovery? International Journal of Computer Vision, 132(8), 3191\u20133207.","journal-title":"International Journal of Computer Vision"},{"key":"2926_CR6","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., & Vedaldi, A. (2014). Describing textures in the wild. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3606\u20133613)","DOI":"10.1109\/CVPR.2014.461"},{"key":"2926_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, (pp. 248\u2013255). Ieee.","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"6","key":"2926_CR8","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L. (2012). The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine, 29(6), 141\u2013142.","journal-title":"IEEE signal processing magazine"},{"key":"2926_CR9","unstructured":"DeVries, T., & Taylor, G. W. (2018). Learning confidence for out-of-distribution detection in neural networks. arXiv preprint, arXiv:1802.04865."},{"key":"2926_CR10","unstructured":"Djurisic, A., Bozanic, N., Ashok, A., & Liu, R. (2023). Extremely simple activation shaping for out-of-distribution detection. In: The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ndYXTEL6cZz."},{"key":"2926_CR11","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=YicbFdNTTy."},{"key":"2926_CR12","doi-asserted-by":"publisher","first-page":"60878","DOI":"10.52202\/075280-2660","volume":"36","author":"X Du","year":"2023","unstructured":"Du, X., Sun, Y., Zhu, J., & Li, Y. (2023). Dream the impossible: Outlier imagination with diffusion models. Advances in Neural Information Processing Systems, 36, 60878\u201360901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2926_CR13","unstructured":"Du, X., Wang, Z., Cai, M., & Li, S. (2022). Towards unknown-aware learning with virtual outlier synthesis. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=TW7d65uYu5M"},{"key":"2926_CR14","unstructured":"Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. In: International conference on machine learning, (pp. 1321\u20131330). PMLR."},{"key":"2926_CR15","unstructured":"Han, X., Papyan, V., & Donoho, D. L. (2022). Neural collapse under MSE loss: Proximity to and dynamics on the central path. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=w1UbdvWH_R3."},{"key":"2926_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"2926_CR17","doi-asserted-by":"publisher","first-page":"5453","DOI":"10.1007\/s11263-024-02139-y","volume":"132","author":"R He","year":"2024","unstructured":"He, R., Han, Z., Nie, X., Yin, Y., & Chang, X. (2024). Visual out-of-distribution detection in open-set noisy environments. International Journal of Computer Vision, 132(11), 5453\u20135470.","journal-title":"International Journal of Computer Vision"},{"key":"2926_CR18","unstructured":"Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hkg4TI9xl."},{"key":"2926_CR19","unstructured":"Hendrycks, D., Mazeika, M., & Dietterich, T. (2019). Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HyxCxhRcY7"},{"key":"2926_CR20","unstructured":"Hendrycks, D., Mazeika, M., Kadavath, S., & Song, D. (2019). Using self-supervised learning can improve model robustness and uncertainty. Advances in neural information processing systems (p. 32)"},{"key":"2926_CR21","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., & Song, D. (2021). Natural adversarial examples. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 15262\u201315271)","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"2926_CR22","doi-asserted-by":"crossref","unstructured":"Hsu, Y. C., Shen, Y., Jin, H., & Kira, Z. (2020). Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10951\u201310960)","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"2926_CR23","doi-asserted-by":"crossref","unstructured":"Hu, D., Alshalali, T. A. N., Xu, X., Yee, L., Shaikh, Z. A., Yang, J., & Gadekallu, T. R. (2025). Pagm: Partially-aligned global and marginal multi-view contrastive clustering for facial recognition in consumer electronics. IEEE Transactions on Consumer Electronics,","DOI":"10.1109\/TCE.2025.3626525"},{"issue":"8","key":"2926_CR24","doi-asserted-by":"publisher","first-page":"4448","DOI":"10.1109\/TFUZZ.2024.3399740","volume":"32","author":"D Hu","year":"2024","unstructured":"Hu, D., Dong, Z., Liang, K., Yu, H., Wang, S., & Liu, X. (2024). High-order topology for deep single-cell multiview fuzzy clustering. IEEE Transactions on Fuzzy Systems, 32(8), 4448\u20134459.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"2926_CR25","doi-asserted-by":"crossref","unstructured":"Hu, D., Guan, R., Dong, Z., Liang, K., Wang, J., Wang, S., & Liu, X. (2025). Single-cell multi-view clustering via community detection with unknown number of clusters. IEEE Transactions on Computational Biology and Bioinformatics.","DOI":"10.1109\/TCBBIO.2025.3636975"},{"issue":"6","key":"2926_CR26","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1093\/bib\/bbae483","volume":"25","author":"D Hu","year":"2024","unstructured":"Hu, D., Guan, R., Liang, K., Yu, H., Quan, H., Zhao, Y., Liu, X., & He, K. (2024). scegg: an exogenous gene-guided clustering method for single-cell transcriptomic data. Briefings in bioinformatics, 25(6), 483.","journal-title":"Briefings in bioinformatics"},{"issue":"2","key":"2926_CR27","doi-asserted-by":"publisher","first-page":"02","DOI":"10.1093\/bib\/bbae102","volume":"25","author":"D Hu","year":"2024","unstructured":"Hu, D., Liang, K., Dong, Z., Wang, J., Zhao, Y., & He, K. (2024). Effective multi-modal clustering method via skip aggregation network for parallel scrna-seq and scatac-seq data. Briefings in Bioinformatics, 25(2), 02.","journal-title":"Briefings in Bioinformatics"},{"key":"2926_CR28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700\u20134708)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2926_CR29","first-page":"677","volume":"34","author":"R Huang","year":"2021","unstructured":"Huang, R., Geng, A., & Li, Y. (2021). On the importance of gradients for detecting distributional shifts in the wild. Advances in Neural Information Processing Systems, 34, 677\u2013689.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2926_CR30","doi-asserted-by":"crossref","unstructured":"Huang, R., & Li, Y. (2021). Mos: Towards scaling out-of-distribution detection for large semantic space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 8710\u20138719)","DOI":"10.1109\/CVPR46437.2021.00860"},{"key":"2926_CR31","doi-asserted-by":"crossref","unstructured":"Jiang, G., Zhu, P., Cao, B., Chen, D., & Hu, Q. (2025). Unknown support prototype set for open set recognition. International Journal of Computer Vision, 1\u201318.","DOI":"10.1007\/s11263-025-02384-9"},{"key":"2926_CR32","doi-asserted-by":"publisher","first-page":"43754","DOI":"10.52202\/075280-1898","volume":"36","author":"JH Kim","year":"2023","unstructured":"Kim, J. H., Yun, S., & Song, H. O. (2023). Neural relation graph: A unified framework for identifying label noise and outlier data. Advances in Neural Information Processing Systems, 36, 43754\u201343779.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2926_CR33","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., & Fei-Fei, L. (2013). 3d object representations for fine-grained categorization. Proceedings of the IEEE international conference on computer vision workshops (pp. 554\u2013561)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"2926_CR34","unstructured":"Krizhevsky, A., Hinton, G., and others (2009). Learning multiple layers of features from tiny images."},{"key":"2926_CR35","unstructured":"Lee, K., Lee, K., Lee, H., & Shin, J. (2018). A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems 31."},{"issue":"12","key":"2926_CR36","doi-asserted-by":"publisher","first-page":"8994","DOI":"10.1109\/TPAMI.2024.3412004","volume":"46","author":"J Li","year":"2024","unstructured":"Li, J., Chen, P., Yu, S., Liu, S., & Jia, J. (2024). Moodv2: Masked image modeling for out-of-distribution detection. IEEE transactions on pattern analysis and machine intelligence, 46(12), 8994\u20139003.","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"2926_CR37","unstructured":"Liang, S., Li, Y., & Srikant, R. (2018). Enhancing the reliability of out-of-distribution image detection in neural networks. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=H1VGkIxRZ."},{"key":"2926_CR38","unstructured":"Liu, L., & Qin, Y. (2024). Fast decision boundary based out-of-distribution detector. ICML."},{"key":"2926_CR39","first-page":"21464","volume":"33","author":"W Liu","year":"2020","unstructured":"Liu, W., Wang, X., Owens, J., & Li, Y. (2020). Energy-based out-of-distribution detection. Advances in neural information processing systems, 33, 21464\u201321475.","journal-title":"Advances in neural information processing systems"},{"key":"2926_CR40","doi-asserted-by":"crossref","unstructured":"Liu, X., Lochman, Y., & Zach, C. (2023). Gen: Pushing the limits of softmax-based out-of-distribution detection. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 23946\u201323955)","DOI":"10.1109\/CVPR52729.2023.02293"},{"key":"2926_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, (pp. 10012\u201310022).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2926_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. Proceedings of the IEEE international conference on computer vision (pp. 3730\u20133738)","DOI":"10.1109\/ICCV.2015.425"},{"issue":"6","key":"2926_CR43","doi-asserted-by":"publisher","first-page":"4534","DOI":"10.1109\/TPAMI.2024.3355212","volume":"46","author":"W Lu","year":"2024","unstructured":"Lu, W., Wang, J., Sun, X., Chen, Y., Ji, X., Yang, Q., & Xie, X. (2024). Diversify: A general framework for time series out-of-distribution detection and generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6), 4534\u20134550.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2926_CR44","unstructured":"Maji, S., Rahtu, E., Kannala, J., Blaschko, M., & Vedaldi, A. (2013). Fine-grained visual classification of aircraft arXiv preprint arXiv:1306.5151."},{"key":"2926_CR45","unstructured":"Ming, Y., Sun, Y., Dia, O., & Li, Y. (2023). How to exploit hyperspherical embeddings for out-of-distribution detection? In: The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=aEFaE0W5pAd"},{"key":"2926_CR46","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y., and others. (2011). Reading digits in natural images with unsupervised feature learning. In: NIPS workshop on deep learning and unsupervised feature learning, vol. 2011, p.\u00a07. Granada."},{"key":"2926_CR47","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 427\u2013436)","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"2926_CR48","unstructured":"Novello, P., Dalmau, J., & And\u00e9ol, L. (2025). Exploring the link between out-of-distribution detection and conformal prediction with illustrations of its benefits. https:\/\/openreview.net\/forum?id=GQhlM0Mavg."},{"key":"2926_CR49","doi-asserted-by":"crossref","unstructured":"Park, J., Jung, Y. G., & Teoh, A. B. J. (2023). Nearest neighbor guidance for out-of-distribution detection. Proceedings of the IEEE\/CVF international conference on computer vision (pp. 1686\u20131695)","DOI":"10.1109\/ICCV51070.2023.00162"},{"key":"2926_CR50","unstructured":"Regmi, S. (2024). Going beyond conventional ood detection. arXiv:2411.10794"},{"key":"2926_CR51","unstructured":"Ren, J., Fort, S., Liu, J., Roy, A. G., Padhy, S., & Lakshminarayanan, B. (2021). A simple fix to mahalanobis distance for improving near-ood detection. ICML Workshop on Uncertainty and Robustness in Deep Learning"},{"key":"2926_CR52","unstructured":"Sagawa, S., Koh, P. W., Hashimoto, T. B., & Liang, P. (2020). Distributionally robust neural networks. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryxGuJrFvS"},{"key":"2926_CR53","unstructured":"Sastry, C. S., & Oore, S. (2020). Detecting out-of-distribution examples with gram matrices. International conference on machine learning (pp. 8491\u20138501) PMLR."},{"key":"2926_CR54","unstructured":"Sehwag, V., Chiang, M., & Mittal, P. (2021). Ssd: A unified framework for self-supervised outlier detection. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=v5gjXpmR8J."},{"key":"2926_CR55","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision (pp. 618\u2013626)","DOI":"10.1109\/ICCV.2017.74"},{"key":"2926_CR56","doi-asserted-by":"publisher","first-page":"17885","DOI":"10.52202\/068431-1300","volume":"35","author":"Y Song","year":"2022","unstructured":"Song, Y., Sebe, N., & Wang, W. (2022). Rankfeat: Rank-1 feature removal for out-of-distribution detection. Advances in Neural Information Processing Systems, 35, 17885\u201317898.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2926_CR57","first-page":"144","volume":"34","author":"Y Sun","year":"2021","unstructured":"Sun, Y., Guo, C., & Li, Y. (2021). React: Out-of-distribution detection with rectified activations. Advances in neural information processing systems, 34, 144\u2013157.","journal-title":"Advances in neural information processing systems"},{"key":"2926_CR58","doi-asserted-by":"crossref","unstructured":"Sun, Y., & Li, Y. (2022). Dice: Leveraging sparsification for out-of-distribution detection. European conference on computer vision (pp. 691\u2013708). Springer.","DOI":"10.1007\/978-3-031-20053-3_40"},{"issue":"2","key":"2926_CR59","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/s11263-024-02184-7","volume":"133","author":"Z Sun","year":"2025","unstructured":"Sun, Z., Chen, S., Yao, T., Yi, R., Ding, S., & Ma, L. (2025). Rethinking open-world deepfake attribution with multi-perspective sensory learning. International Journal of Computer Vision, 133(2), 628\u2013651.","journal-title":"International Journal of Computer Vision"},{"key":"2926_CR60","first-page":"11839","volume":"33","author":"J Tack","year":"2020","unstructured":"Tack, J., Mo, S., Jeong, J., & Shin, J. (2020). Csi: Novelty detection via contrastive learning on distributionally shifted instances. Advances in neural information processing systems, 33, 11839\u201311852.","journal-title":"Advances in neural information processing systems"},{"key":"2926_CR61","unstructured":"Tao, L., Du, X., Zhu, J., & Li, Y. Non-parametric outlier synthesis. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=JHklpEZqduQ"},{"key":"2926_CR62","doi-asserted-by":"crossref","unstructured":"Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018). The inaturalist species classification and detection dataset. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8769\u20138778)","DOI":"10.1109\/CVPR.2018.00914"},{"key":"2926_CR63","unstructured":"Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic learning in a random world. Springer."},{"key":"2926_CR64","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Z., Feng, L., & Zhang, W. (2022). Vim: Out-of-distribution with virtual-logit matching. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4921\u20134930)","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"2926_CR65","doi-asserted-by":"crossref","unstructured":"Wang, Y., Mu, J., Huang, H., Wang, Q., Zhu, P., & Hu, Q. Backmix: Regularizing open set recognition by removing underlying fore-background priors. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)","DOI":"10.1109\/TPAMI.2025.3550703"},{"key":"2926_CR66","unstructured":"Wei, H., Xie, R., Cheng, H., Feng, L., An, B., & Li, Y. (2022). Mitigating neural network overconfidence with logit normalization. International conference on machine learning (pp. 23631\u201323644) PMLR."},{"issue":"3","key":"2926_CR67","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1109\/TPAMI.2022.3201541","volume":"46","author":"J Willes","year":"2022","unstructured":"Willes, J., Harrison, J., Harakeh, A., Finn, C., Pavone, M., & Waslander, S. L. (2022). Bayesian embeddings for few-shot open world recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(3), 1513\u20131529.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2926_CR68","doi-asserted-by":"crossref","unstructured":"Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010). Sun database: Large-scale scene recognition from abbey to zoo. IEEE computer society conference on computer vision and pattern recognition (pp. 3485\u20133492). IEEE.","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"2926_CR69","unstructured":"Xu, K., Chen, R., Franchi, G., & Yao, A. Scaling for training time and post-hoc out-of-distribution detection enhancement. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=RDSTjtnqCg"},{"key":"2926_CR70","unstructured":"Xu, P., Ehinger, K. A., Zhang, Y., Finkelstein, A., Kulkarni, S. R., & Xiao, J. (2015). Turkergaze: Crowdsourcing saliency with webcam based eye tracking arXiv preprint arXiv:1504.06755."},{"issue":"8","key":"2926_CR71","doi-asserted-by":"publisher","first-page":"3208","DOI":"10.1007\/s11263-024-02015-9","volume":"132","author":"Z Yang","year":"2024","unstructured":"Yang, Z., Yue, J., Ghamisi, P., Zhang, S., Ma, J., & Fang, L. (2024). Open set recognition in real world. International Journal of Computer Vision, 132(8), 3208\u20133231.","journal-title":"International Journal of Computer Vision"},{"key":"2926_CR72","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks (p. BMVC)","DOI":"10.5244\/C.30.87"},{"key":"2926_CR73","unstructured":"Zhang, J., Fu, Q., Chen, X., Du, L., Li, Z., Wang, G., Han, S., Zhang, D., and others. (2022). Out-of-distribution detection based on in-distribution data patterns memorization with modern hopfield energy. In: The Eleventh International Conference on Learning Representations."},{"key":"2926_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, J., Inkawhich, N., Linderman, R., Chen, Y., & Li, H. (2023). Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments. Proceedings of the IEEE\/CVF winter conference on applications of computer vision (pp. 5531\u20135540)","DOI":"10.1109\/WACV56688.2023.00549"},{"key":"2926_CR75","unstructured":"Zhu, Z., Ding, T., Zhou, J., Li, X., You, C., Sulam, J., & Qu, Q. (2021). A geometric analysis of neural collapse with unconstrained features. Advances in Neural Information Processing Systems,34, 29820\u201329834."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02926-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02926-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02926-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:50:05Z","timestamp":1785304205000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02926-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":75,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["2926"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02926-9","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"6 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","label":"Conflicts of Interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"348"}}