{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:04:41Z","timestamp":1783969481907,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819233991","type":"print"},{"value":"9789819234004","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3400-4_37","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:15:35Z","timestamp":1783966535000},"page":"450-461","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DHCA-Net: A Dual-Branch Multi-Task Network Based on Heterogeneous Collaborative Attention for FacialExpression Recognition"],"prefix":"10.1007","author":[{"given":"Haotian","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengye","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"3","key":"37_CR1","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1109\/TAFFC.2020.2981446","volume":"13","author":"S Li","year":"2022","unstructured":"Li, S., Deng, W.: Deep facial expression recognition: a survey. IEEE Trans. Affect. Comput. 13(3), 1195\u20131215 (2022)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"37_CR2","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang, K., Peng, X., Yang, J., Meng, D., Qiao, Y.: Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057\u20134069 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"37_CR3","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/TAFFC.2021.3122146","volume":"14","author":"F Ma","year":"2023","unstructured":"Ma, F., Sun, B., Li, S.: Facial expression recognition with visual transformers and attentional selective fusion. IEEE Trans. Affect. Comput. 14(2), 1236\u20131248 (2023)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., Qiao, Y.: Suppressing uncertainties for large-scale facial expression recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6896\u20136905 (2020)","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Huang, J., Qu, L., Jia, R., Zhao, B.: O2U-Net: a simple noisy label detection approach for deep neural networks. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3325\u20133333 (2019)","DOI":"10.1109\/ICCV.2019.00342"},{"key":"37_CR6","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.patrec.2022.10.016","volume":"164","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Sun, X., Li, J., Wang, M.: MAN: mining ambiguity and noise for facial expression recognition in the wild. Pattern Recogn. Lett. 164, 23\u201329 (2022)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"37_CR7","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1109\/TAFFC.2024.3382618","volume":"15","author":"C Yu","year":"2024","unstructured":"Yu, C., Zhang, D., Zou, W., Li, M.: Joint training on multiple datasets with inconsistent labeling criteria for facial expression recognition. IEEE Trans. Affect. Comput. 15(3), 1812\u20131824 (2024)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"37_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41\u201375 (1997)","journal-title":"Mach. Learn."},{"key":"37_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108401","volume":"123","author":"W Yu","year":"2021","unstructured":"Yu, W., Xu, H.: Co-attentive multi-task convolutional neural network for facial expression recognition. Pattern Recogn. 123, 108401 (2021)","journal-title":"Pattern Recogn."},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3994\u20134003 (2016)","DOI":"10.1109\/CVPR.2016.433"},{"key":"37_CR11","unstructured":"Azadi, S., Feng, J., Jegelka, S., Darrell, T.: Auxiliary image regularization for deep CNNs with noisy labels. arXiv preprint https:\/\/arxiv.org\/abs\/1511.07069 (2015)"},{"key":"37_CR12","unstructured":"Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Zeng, J., Shan, S., Chen, X.: Facial expression recognition with inconsistently annotated datasets. In: European Conference on Computer Vision (ECCV), pp. 222\u2013237 (2018)","DOI":"10.1007\/978-3-030-01261-8_14"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, J., Chen, Y., Shi, Z., Geng, X., Rui, Y.: Label distribution learning on auxiliary label space graphs for facial expression recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13984\u201313993 (2020)","DOI":"10.1109\/CVPR42600.2020.01400"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Farzaneh, A.H., Qi, X.: Facial expression recognition in the wild via deep attentive center loss. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2402\u20132411 (2021)","DOI":"10.1109\/WACV48630.2021.00245"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"She, J., et al.: Dive into ambiguity: latent distribution mining and pairwise uncertainty estimation for facial expression recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6248\u20136257 (2021)","DOI":"10.1109\/CVPR46437.2021.00618"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Ruan, D., Yan, Y., Lai, S., Chai, Z., Shen, C., Wang, H.: Feature decomposition and reconstruction learning for effective facial expression recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7660\u20137669 (2021)","DOI":"10.1109\/CVPR46437.2021.00757"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Xue, F., Wang, Q., Guo, G.: TransFER: learning relation-aware facial expression representations with transformers. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3601\u20133610 (2021)","DOI":"10.1109\/ICCV48922.2021.00358"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Wu, Z., Cui, J.: LA-Net: landmark-aware learning for reliable facial expression recognition under label noise. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 20698\u201320707 (2023)","DOI":"10.1109\/ICCV51070.2023.01892"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Le, N., et al.: Uncertainty-aware label distribution learning for facial expression recognition. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6088\u20136097 (2023)","DOI":"10.1109\/WACV56688.2023.00603"},{"key":"37_CR21","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.neunet.2023.11.033","volume":"170","author":"H Tao","year":"2024","unstructured":"Tao, H., Duan, Q.: Hierarchical attention network with progressive feature fusion for facial expression recognition. Neural Netw. 170, 337\u2013348 (2024)","journal-title":"Neural Netw."},{"key":"37_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110951","volume":"157","author":"J Mao","year":"2025","unstructured":"Mao, J., Xu, R., Yin, X., Chang, Y., Nie, B., Huang, Y.: POSTER++: a simpler and stronger facial expression recognition network. Pattern Recogn. 157, 110951 (2025)","journal-title":"Pattern Recogn."},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: European Conference on Computer Vision (ECCV), pp. 87\u2013102 (2016)","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, Y., Gao, X., Han, Z.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Chinese Conference on Biometric Recognition (CCBR), pp. 428\u2013438 (2018)","DOI":"10.1007\/978-3-319-97909-0_46"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Feng, Z.H., Kittler, J., Awais, M., Huber, P., Wu, X.J.: Wing loss for robust facial landmark localisation with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2235\u20132245 (2018)","DOI":"10.1109\/CVPR.2018.00238"},{"key":"37_CR28","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., Du, J.: Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.277"},{"issue":"1","key":"37_CR31","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2019","unstructured":"Mollahosseini, A., Hasani, B., Mahoor, M.H.: AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18\u201331 (2019)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Lee, J., Choi, Y., Kim, H., Park, S., Kang, B., Kim, T.: Navigating label ambiguity for facial expression recognition in the wild. In: AAAI Conference on Artificial Intelligence (AAAI) (2025)","DOI":"10.1609\/aaai.v39i4.32476"},{"key":"37_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.104901","volume":"143","author":"H Shin","year":"2024","unstructured":"Shin, H., Lee, B., Ku, B., Ko, H.: Noisy label facial expression recognition via face-specific label distribution learning. Image Vis. Comput. 143, 104901 (2024)","journal-title":"Image Vis. Comput."},{"issue":"3","key":"37_CR34","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.1109\/TAFFC.2025.3549017","volume":"16","author":"X Zhang","year":"2025","unstructured":"Zhang, X., et al.: ReSup: reliable label noise suppression for facial expression recognition. IEEE Trans. Affect. Comput. 16(3), 2006\u20132019 (2025)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"37_CR35","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (ICLR) (2019)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3400-4_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:15:37Z","timestamp":1783966537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3400-4_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819233991","9789819234004"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3400-4_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}