{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:27:57Z","timestamp":1764588477768,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176139"],"award-info":[{"award-number":["62176139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Major Basic Research Project of Natural Science Foundation of Shandong Province","award":["ZR2021ZD15"],"award-info":[{"award-number":["ZR2021ZD15"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3611720","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:40Z","timestamp":1698391660000},"page":"8105-8113","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["LHAct: Rectifying Extremely Low and High Activations for Out-of-Distribution Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0321-9577","authenticated-orcid":false,"given":"Yue","family":"Yuan","sequence":"first","affiliation":[{"name":"Shandong University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5354-9644","authenticated-orcid":false,"given":"Rundong","family":"He","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2851-193X","authenticated-orcid":false,"given":"Zhongyi","family":"Han","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Masdar, UAE"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-1294","authenticated-orcid":false,"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86523-8_26"},{"key":"e_1_3_2_2_2_1","volume-title":"Describing Textures in the Wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition. 3606--3613","author":"Cimpoi Mircea","year":"2014","unstructured":"Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. 2014. Describing Textures in the Wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition. 3606--3613. https:\/\/doi.org\/10.1109\/ CVPR.2014.461"},{"key":"e_1_3_2_2_3_1","volume-title":"Reducing network agnostophobia. Advances in Neural Information Processing Systems 31","author":"Dhamija Akshay Raj","year":"2018","unstructured":"Akshay Raj Dhamija, Manuel G\u00fcnther, and Terrance Boult. 2018. Reducing network agnostophobia. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_2_4_1","volume-title":"VOS: Learning What You Don't Know by Virtual Outlier Synthesis. arXiv preprint arXiv:2202.01197","author":"Du Xuefeng","year":"2022","unstructured":"Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li. 2022. VOS: Learning What You Don't Know by Virtual Outlier Synthesis. arXiv preprint arXiv:2202.01197 (2022)."},{"key":"e_1_3_2_2_5_1","volume-title":"Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems 34","author":"Fort Stanislav","year":"2021","unstructured":"Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. 2021. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_6_1","volume-title":"Igeood: An Information Geometry Approach to Out-of-Distribution Detection. arXiv preprint arXiv:2203.07798","author":"Camara Gomes Eduardo Dadalto","year":"2022","unstructured":"Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, and Pablo Piantanida. 2022. Igeood: An Information Geometry Approach to Out-of-Distribution Detection. arXiv preprint arXiv:2203.07798 (2022)."},{"key":"e_1_3_2_2_7_1","volume-title":"Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385","author":"He Kaiming","year":"2015","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385 (2015). arXiv:1512.03385 http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01418"},{"key":"e_1_3_2_2_9_1","volume-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)."},{"key":"e_1_3_2_2_10_1","volume-title":"Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606","author":"Hendrycks Dan","year":"2018","unstructured":"Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. 2018. Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606 (2018)."},{"key":"e_1_3_2_2_11_1","volume-title":"Yang Song, Alexander Shepard, Hartwig Adam, Pietro Perona, and Serge J. Belongie.","author":"Horn Grant Van","year":"2017","unstructured":"Grant Van Horn, Oisin Mac Aodha, Yang Song, Alexander Shepard, Hartwig Adam, Pietro Perona, and Serge J. Belongie. 2017. The iNaturalist Challenge 2017 Dataset. CoRR abs\/1707.06642 (2017). arXiv:1707.06642 http:\/\/arxiv.org\/abs\/1707. 06642"},{"key":"e_1_3_2_2_12_1","volume-title":"On the Importance of Gradients for Detecting Distributional Shifts in the Wild. arXiv preprint arXiv:2110.00218","author":"Huang Rui","year":"2021","unstructured":"Rui Huang, Andrew Geng, and Yixuan Li. 2021. On the Importance of Gradients for Detecting Distributional Shifts in the Wild. arXiv preprint arXiv:2110.00218 (2021)."},{"key":"e_1_3_2_2_13_1","volume-title":"MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space. CoRR abs\/2105.01879","author":"Huang Rui","year":"2021","unstructured":"Rui Huang and Yixuan Li. 2021. MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space. CoRR abs\/2105.01879 (2021). arXiv:2105.01879 https:\/\/arxiv.org\/abs\/2105.01879"},{"key":"e_1_3_2_2_14_1","volume-title":"International Conference on Learning Representations.","author":"Jiang Dihong","year":"2021","unstructured":"Dihong Jiang, Sun Sun, and Yaoliang Yu. 2021. Revisiting flow generative models for Out-of-distribution detection. In International Conference on Learning Representations."},{"volume-title":"Cognitive Systems and Information Processing","author":"Kong Haojia","key":"e_1_3_2_2_15_1","unstructured":"Haojia Kong and Haoan Li. 2023. BFAct: Out-of-Distribution Detection with Butterworth Filter Rectified Activations. In Cognitive Systems and Information Processing, Fuchun Sun, Angelo Cangelosi, Jianwei Zhang, Yuanlong Yu, Huaping Liu, and Bin Fang (Eds.). Springer Nature Singapore, Singapore, 115--129."},{"key":"e_1_3_2_2_16_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Technical Report 0. University of Toronto Toronto Ontario."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_3_2_2_18_1","volume-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems 31","author":"Lee Kimin","year":"2018","unstructured":"Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01323"},{"key":"e_1_3_2_2_20_1","volume-title":"Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690","author":"Liang Shiyu","year":"2017","unstructured":"Shiyu Liang, Yixuan Li, and Rayadurgam Srikant. 2017. Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)."},{"key":"e_1_3_2_2_21_1","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu Weitang","year":"2020","unstructured":"Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. Advances in Neural Information Processing Systems 33 (2020), 21464--21475.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_22_1","volume-title":"Energy-based out-of-distribution detection. arXiv preprint arXiv:2010.03759","author":"Liu Weitang","year":"2020","unstructured":"Weitang Liu, Xiaoyun Wang, John D Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. arXiv preprint arXiv:2010.03759 (2020)."},{"key":"e_1_3_2_2_23_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3232--3240","author":"Morningstar Warren","year":"2021","unstructured":"Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, and Joshua Dillon. 2021. Density of states estimation for out of distribution detection. In International Conference on Artificial Intelligence and Statistics. PMLR, 3232--3240."},{"key":"e_1_3_2_2_24_1","volume-title":"Provable Guarantees for Understanding Out-of-distribution Detection. arXiv preprint arXiv:2112.00787","author":"Morteza Peyman","year":"2021","unstructured":"Peyman Morteza and Yixuan Li. 2021. Provable Guarantees for Understanding Out-of-distribution Detection. arXiv preprint arXiv:2112.00787 (2021)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378--3758(99)00096-8"},{"key":"e_1_3_2_2_26_1","volume-title":"Reading Digits in Natural Images with Unsupervised Feature Learning. NIPS (01","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Ng. 2011. Reading Digits in Natural Images with Unsupervised Feature Learning. NIPS (01 2011)."},{"key":"e_1_3_2_2_27_1","volume-title":"Likelihood ratios for out-of-distribution detection. Advances in neural information processing systems 32","author":"Ren Jie","year":"2019","unstructured":"Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan. 2019. Likelihood ratios for out-of-distribution detection. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_28_1","volume-title":"Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. CoRR abs\/1801.04381","author":"Sandler Mark","year":"2018","unstructured":"Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. CoRR abs\/1801.04381 (2018). arXiv:1801.04381 http:\/\/arxiv.org\/abs\/1801.04381"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.678493"},{"key":"e_1_3_2_2_30_1","volume-title":"RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection. arXiv preprint arXiv:2209.08590","author":"Song Yue","year":"2022","unstructured":"Yue Song, Nicu Sebe, and Wei Wang. 2022. RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection. arXiv preprint arXiv:2209.08590 (2022)."},{"key":"e_1_3_2_2_31_1","volume-title":"React: Out-of-distribution detection with rectified activations. Advances in Neural Information Processing Systems 34","author":"Sun Yiyou","year":"2021","unstructured":"Yiyou Sun, Chuan Guo, and Yixuan Li. 2021. React: Out-of-distribution detection with rectified activations. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_32_1","volume-title":"Out-of-distribution Detection with Deep Nearest Neighbors. arXiv preprint arXiv:2204.06507","author":"Sun Yiyou","year":"2022","unstructured":"Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li. 2022. Out-of-distribution Detection with Deep Nearest Neighbors. arXiv preprint arXiv:2204.06507 (2022)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547876"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3251025"},{"key":"e_1_3_2_2_35_1","volume-title":"C3D: Generic Features for Video Analysis. CoRR abs\/1412.0767","author":"Tran Du","year":"2014","unstructured":"Du Tran, Lubomir D. Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. 2014. C3D: Generic Features for Video Analysis. CoRR abs\/1412.0767 (2014). arXiv:1412.0767 http:\/\/arxiv.org\/abs\/1412.0767"},{"key":"e_1_3_2_2_36_1","volume-title":"Mitigating Neural Network Overconfidence with Logit Normalization. arXiv preprint arXiv:2205.09310","author":"Wei Hongxin","year":"2022","unstructured":"Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. 2022. Mitigating Neural Network Overconfidence with Logit Normalization. arXiv preprint arXiv:2205.09310 (2022)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"e_1_3_2_2_38_1","volume-title":"Likelihood regret: An out-of-distribution detection score for variational auto-encoder. Advances in neural information processing systems 33","author":"Xiao Zhisheng","year":"2020","unstructured":"Zhisheng Xiao, Qing Yan, and Yali Amit. 2020. Likelihood regret: An out-of-distribution detection score for variational auto-encoder. Advances in neural information processing systems 33 (2020), 20685--20696."},{"key":"e_1_3_2_2_39_1","volume-title":"TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking. CoRR abs\/1504.06755","author":"Xu Pingmei","year":"2015","unstructured":"Pingmei Xu, Krista A. Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R. Kulkarni, and Jianxiong Xiao. 2015. TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking. CoRR abs\/1504.06755 (2015). arXiv:1504.06755 http:\/\/arxiv.org\/abs\/1504.06755"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548012"},{"key":"e_1_3_2_2_41_1","volume-title":"CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification. In The 40th International Conference on Machine Learning.","author":"Yin Nan","year":"2023","unstructured":"Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, and Xiao Luo. 2023. CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification. In The 40th International Conference on Machine Learning."},{"key":"e_1_3_2_2_42_1","volume-title":"OMG: Towards Effective Graph Classification Against Label Noise","author":"Yin Nan","year":"2023","unstructured":"Nan Yin, Li Shen, Mengzhu Wang, Xiao Luo, Zhigang Luo, and Dacheng Tao. 2023. OMG: Towards Effective Graph Classification Against Label Noise. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_2_43_1","volume-title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. CoRR abs\/1506.03365","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. 2015. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. CoRR abs\/1506.03365 (2015). arXiv:1506.03365 http:\/\/arxiv.org\/abs\/ 1506.03365"},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Machine Learning. PMLR, 12427--12436","author":"Zhang Lily","year":"2021","unstructured":"Lily Zhang, Mark Goldstein, and Rajesh Ranganath. 2021. Understanding failures in out-of-distribution detection with deep generative models. In International Conference on Machine Learning. PMLR, 12427--12436."},{"key":"e_1_3_2_2_45_1","volume-title":"Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. In International Conference on Machine Learning. PMLR, 12803--12812","author":"Zhou Aurick","year":"2021","unstructured":"Aurick Zhou and Sergey Levine. 2021. Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. In International Conference on Machine Learning. PMLR, 12803--12812."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"e_1_3_2_2_47_1","unstructured":"Yao Zhu Yuefeng Chen Xiaodan Li Rong Zhang Hui Xue Xiang Tian Rongxin Jiang Bolun Zheng and Yaowu Chen. 2022. Rethinking Out-of-Distribution Detection From a Human-Centric Perspective. arXiv:2211.16778 [cs.CV]"},{"key":"e_1_3_2_2_48_1","volume-title":"bolun zheng, and Yaowu Chen","author":"Zhu Yao","year":"2022","unstructured":"Yao Zhu, YueFeng Chen, Chuanlong Xie, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, bolun zheng, and Yaowu Chen. 2022. Boosting Out-of-distribution Detection with Typical Features. arXiv:2210.04200 [cs.CV]"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Ottawa ON Canada","acronym":"MM '23"},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611720","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3611720","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:06:43Z","timestamp":1755821203000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":48,"alternative-id":["10.1145\/3581783.3611720","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3611720","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}