{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T10:59:06Z","timestamp":1785236346502,"version":"3.55.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781216","type":"print"},{"value":"9783031781223","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"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\/978-3-031-78122-3_1","type":"book-chapter","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T07:16:42Z","timestamp":1733296602000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Multi-order Context-Aware Kernel Network for\u00a0Multi-label Classification"],"prefix":"10.1007","author":[{"given":"Mingyuan","family":"Jiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hichem","family":"Sahbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"issue":"10","key":"1_CR1","first-page":"127","volume":"7","author":"R Alazaidah","year":"2016","unstructured":"Alazaidah, R., Ahmad, F.K.: Trending challenges in multi label classification. Int. J. Adv. Comput. Sci. Appl. 7(10), 127\u2013131 (2016)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Chen, T., Wang, Z., Li, G., Lin, L.: Recurrent attentional reinforcement learning for multi-label image recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.12281"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/TIP.2022.3148867","volume":"31","author":"ZM Chen","year":"2022","unstructured":"Chen, Z.M., Cui, Q., Zhao, B., Song, R., Zhang, X., Yoshie, O.: SST: spatial and semantic transformers for multi-label image recognition. IEEE Trans. Image Process. 31, 2570\u20132583 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z.M., Wei, X.S., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5177\u20135186 (2019)","DOI":"10.1109\/CVPR.2019.00532"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1_CR6","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Guo, H., Zheng, K., Fan, X., Yu, H., Wang, S.: Visual attention consistency under image transforms for multi-label image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 729\u2013739 (2019)","DOI":"10.1109\/CVPR.2019.00082"},{"issue":"4","key":"1_CR8","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1109\/TIP.2017.2666038","volume":"26","author":"M Jiu","year":"2017","unstructured":"Jiu, M., Sahbi, H.: Nonlinear deep kernel learning for image annotation. IEEE Trans. Image Process. 26(4), 1820\u20131832 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.patcog.2018.12.005","volume":"88","author":"M Jiu","year":"2019","unstructured":"Jiu, M., Sahbi, H.: Deep representation design from deep kernel networks. Pattern Recogn. 88, 447\u2013457 (2019)","journal-title":"Pattern Recogn."},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.neucom.2021.12.006","volume":"474","author":"M Jiu","year":"2022","unstructured":"Jiu, M., Sahbi, H.: Context-aware deep kernel networks for image annotation. Neurocomputing 474, 154\u2013167 (2022)","journal-title":"Neurocomputing"},{"key":"1_CR11","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.patrec.2013.09.021","volume":"50","author":"M Jiu","year":"2014","unstructured":"Jiu, M., Wolf, C., Taylor, G., Baskurt, A.: Human body part estimation from depth images via spatially-constrained deep learning. Pattern Recogn. Lett. 50, 122\u2013129 (2014)","journal-title":"Pattern Recogn. Lett."},{"key":"1_CR12","unstructured":"Kim, J.H., Jun, J., Zhang, B.T.: Bilinear attention networks. Adv. Neural Inf. Proce Syst. 31 (2018)"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Lanchantin, J., Wang, T., Ordonez, V., Qi, Y.: General multi-label image classification with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16478\u201316488 (2021)","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Li, X., Sahbi, H.: Superpixel-based object class segmentation using conditional random fields. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1101\u20131104. IEEE (2011)","DOI":"10.1109\/ICASSP.2011.5946600"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, L.: More correlations better performance: fully associative networks for multi-label image classification. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9437\u20139444. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412004"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1_CR17","unstructured":"Liu, S., Zhang, L., Yang, X., Su, H., Zhu, J.: Query2label: A simple transformer way to multi-label classification. arXiv preprint arXiv:2107.10834 (2021)"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Ma, L., Sun, D., Wang, L., Zhao, H., Luo, B.: Semantic-aware dual contrastive learning for multi-label image classification. arXiv preprint arXiv:2307.09715 (2023)","DOI":"10.3233\/FAIA230449"},{"key":"1_CR19","unstructured":"Mazari, A., Sahbi, H.: Mlgcn: Multi-laplacian graph convolutional networks for human action recognition. In: The British Machine Vision Conference (BMVC) (2019)"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Murthy, V.N., Maji, S., Manmatha, R.: Automatic image annotation using deep learning representations. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 603\u2013606 (2015)","DOI":"10.1145\/2671188.2749391"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/S0079-6123(06)55002-2","volume":"155","author":"A Oliva","year":"2006","unstructured":"Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. Prog. Brain Res. 155, 23\u201336 (2006)","journal-title":"Prog. Brain Res."},{"key":"1_CR22","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural Inf. Proce. Syst. 28 (2015)"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Ridnik, T., et al.: Asymmetric loss for multi-label classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 82\u201391 (2021)","DOI":"10.1109\/ICCV48922.2021.00015"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Ridnik, T., Lawen, H., Noy, A., Ben\u00a0Baruch, E., Sharir, G., Friedman, I.: Tresnet: high performance gpu-dedicated architecture. In: proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1400\u20131409 (2021)","DOI":"10.1109\/WACV48630.2021.00144"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s13735-015-0082-3","volume":"4","author":"H Sahbi","year":"2015","unstructured":"Sahbi, H.: Imageclef annotation with explicit context-aware kernel maps. Int. J. Multimedia Inf. Retrieval 4, 113\u2013128 (2015)","journal-title":"Int. J. Multimedia Inf. Retrieval"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Sahbi, H.: Learning laplacians in chebyshev graph convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2064\u20132075 (2021)","DOI":"10.1109\/ICCVW54120.2021.00234"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Sahbi, H., Li, X.: Context-based support vector machines for interconnected image annotation. In: Asian Conference on Computer Vision, pp. 214\u2013227. Springer (2010)","DOI":"10.1007\/978-3-642-19315-6_17"},{"issue":"1","key":"1_CR28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"1_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Tamura, M., Ohashi, H., Yoshinaga, T.: Qpic: query-based pairwise human-object interaction detection with image-wide contextual information. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10410\u201310419 (2021)","DOI":"10.1109\/CVPR46437.2021.01027"},{"key":"1_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Proce. Syst. 30 (2017)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2285\u20132294 (2016)","DOI":"10.1109\/CVPR.2016.251"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Multi-label classification with label graph superimposing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a034, pp. 12265\u201312272 (2020)","DOI":"10.1609\/aaai.v34i07.6909"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, T., Li, G., Xu, R., Lin, L.: Multi-label image recognition by recurrently discovering attentional regions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 464\u2013472 (2017)","DOI":"10.1109\/ICCV.2017.58"},{"issue":"9","key":"1_CR35","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1109\/TPAMI.2015.2491929","volume":"38","author":"Y Wei","year":"2015","unstructured":"Wei, Y., et al.: Hcp: a flexible CNN framework for multi-label image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1901\u20131907 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 22\u201331 (2021)","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Wu, Y., Feng, S., Wang, Y.: Semantic-aware graph matching mechanism for multi-label image recognition. IEEE Trans. Circuits Syst. Video Technol. (2023)","DOI":"10.1109\/TCSVT.2023.3268997"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, H., Feng, S., Jin, Y., Lyu, G., Wu, Z.: Gm-mlic: graph matching based multi-label image classification. arXiv preprint arXiv:2104.14762 (2021)","DOI":"10.24963\/ijcai.2021\/163"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"You, R., Guo, Z., Cui, L., Long, X., Bao, Y., Wen, S.: Cross-modality attention with semantic graph embedding for multi-label classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a034, pp. 12709\u201312716 (2020)","DOI":"10.1609\/aaai.v34i07.6964"},{"key":"1_CR40","doi-asserted-by":"publisher","first-page":"22385","DOI":"10.1007\/s11042-018-5973-x","volume":"77","author":"W Zhang","year":"2018","unstructured":"Zhang, W., Hu, H., Hu, H.: Neural ranking for automatic image annotation. Multimedia Tools Appl. 77, 22385\u201322406 (2018)","journal-title":"Multimedia Tools Appl."},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Zhu, F., Li, H., Ouyang, W., Yu, N., Wang, X.: Learning spatial regularization with image-level supervisions for multi-label image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5513\u20135522 (2017)","DOI":"10.1109\/CVPR.2017.219"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78122-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T08:06:32Z","timestamp":1733299592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78122-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"ISBN":["9783031781216","9783031781223"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78122-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]},"assertion":[{"value":"5 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}