{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:19:29Z","timestamp":1766067569118,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20200581"],"award-info":[{"award-number":["BK20200581"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806220"],"award-info":[{"award-number":["61806220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s11554-022-01228-w","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T19:03:08Z","timestamp":1656615788000},"page":"853-866","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient multi-granularity network for fine-grained image classification"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3706-9912","authenticated-orcid":false,"given":"Jiabao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"1228_CR1","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neucom.2021.10.015","volume":"467","author":"VM Ara\u00fajo","year":"2022","unstructured":"Ara\u00fajo, V.M., Oliveira, L.S., Koerich, A.L.: Two-view fine-grained classification of plant species. Neurocomputing 467, 427\u2013441 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.10.015","journal-title":"Neurocomputing"},{"key":"1228_CR2","doi-asserted-by":"publisher","first-page":"107682","DOI":"10.1016\/j.knosys.2021.107682","volume":"236","author":"S Barshandeh","year":"2022","unstructured":"Barshandeh, S., Dana, R., Eskandarian, P.: A learning automata-based hybrid MPA and JS algorithm for numerical optimization problems and its application on data clustering. Knowl. Based Syst. 236, 107682 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2021.107682","journal-title":"Knowl. Based Syst."},{"issue":"4","key":"1228_CR3","doi-asserted-by":"publisher","first-page":"3079","DOI":"10.1007\/s00366-020-00994-0","volume":"37","author":"S Barshandeh","year":"2021","unstructured":"Barshandeh, S., Haghzadeh, M.: A new hybrid chaotic atom search optimization based on tree-seed algorithm and levy flight for solving optimization problems. Eng. Comput. 37(4), 3079\u20133122 (2021). https:\/\/doi.org\/10.1007\/s00366-020-00994-0","journal-title":"Eng. Comput."},{"key":"1228_CR4","doi-asserted-by":"publisher","first-page":"4683","DOI":"10.1109\/TIP.2020.2973812","volume":"29","author":"D Chang","year":"2020","unstructured":"Chang, D., Ding, Y., Xie, J., Bhunia, A.K., Li, X., Ma, Z., Wu, M., Guo, J., Song, Y.: The devil is in the channels: Mutual-channel loss for fine-grained image classification. IEEE Trans. Image Process. 29, 4683\u20134695 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2973812","journal-title":"IEEE Trans. Image Process."},{"key":"1228_CR5","doi-asserted-by":"publisher","unstructured":"Chen, Y., Bai, Y., Zhang, W., Mei, T.: Destruction and construction learning for fine-grained image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 5157\u20135166. Computer Vision Foundation \/ IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00530","DOI":"10.1109\/CVPR.2019.00530"},{"key":"1228_CR6","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01097","author":"G Dimitriadis","year":"2018","unstructured":"Dimitriadis, G., Neto, J.P., Kampff, A.R.: t-sne visualization of large-scale neural recordings. Neural Comput. (2018). https:\/\/doi.org\/10.1162\/neco_a_01097","journal-title":"Neural Comput."},{"key":"1228_CR7","doi-asserted-by":"publisher","unstructured":"Ding, Y., Zhou, Y., Zhu, Y., Ye, Q., Jiao, J.: Selective sparse sampling for fine-grained image recognition. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pp. 6598\u20136607. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00670","DOI":"10.1109\/ICCV.2019.00670"},{"key":"1228_CR8","doi-asserted-by":"publisher","unstructured":"Du, R., Chang, D., Bhunia, A.K., Xie, J., Ma, Z., Song, Y., Guo, J.: Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. In: A.\u00a0Vedaldi, H.\u00a0Bischof, T.\u00a0Brox, J.\u00a0Frahm (eds.) Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XX, Lecture Notes in Computer Science, vol. 12365, pp. 153\u2013168. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_10","DOI":"10.1007\/978-3-030-58565-5_10"},{"key":"1228_CR9","doi-asserted-by":"publisher","unstructured":"Engin, M., Wang, L., Zhou, L., Liu, X.: Deepkspd: Learning kernel-matrix-based SPD representation for fine-grained image recognition. In: V.\u00a0Ferrari, M.\u00a0Hebert, C.\u00a0Sminchisescu, Y.\u00a0Weiss (eds.) Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part II, Lecture Notes in Computer Science, vol. 11206, pp. 629\u2013645. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_38","DOI":"10.1007\/978-3-030-01216-8_38"},{"key":"1228_CR10","doi-asserted-by":"crossref","unstructured":"Gao, Y., Han, X., Wang, X., Huang, W., Scott, M.: Channel interaction networks for fine-grained image categorization. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 10818\u201310825. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i07.6712"},{"key":"1228_CR11","unstructured":"He, J., Chen, J., Liu, S., Kortylewski, A., Yang, C., Bai, Y., Wang, C., Yuille, A.L.: Transfg: A transformer architecture for fine-grained recognition. CoRR abs\/2103.07976 (2021)"},{"key":"1228_CR12","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp. 770\u2013778. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1228_CR13","doi-asserted-by":"publisher","unstructured":"Huang, S., Wang, X., Tao, D.: Stochastic partial swap: Enhanced model generalization and interpretability for fine-grained recognition. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pp. 600\u2013609. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00066","DOI":"10.1109\/ICCV48922.2021.00066"},{"key":"1228_CR14","doi-asserted-by":"publisher","unstructured":"Ji, R., Wen, L., Zhang, L., Du, D., Wu, Y., Zhao, C., Liu, X., Huang, F.: Attention convolutional binary neural tree for fine-grained visual categorization. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pp. 10465\u201310474. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01048","DOI":"10.1109\/CVPR42600.2020.01048"},{"issue":"6","key":"1228_CR15","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TPAMI.2017.2723400","volume":"40","author":"T Lin","year":"2018","unstructured":"Lin, T., RoyChowdhury, A., Maji, S.: Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1309\u20131322 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2723400","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1228_CR16","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pp. 9992\u201310002. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1228_CR17","doi-asserted-by":"publisher","unstructured":"Luo, W., Yang, X., Mo, X., Lu, Y., Davis, L., Li, J., Yang, J., Lim, S.: Cross-x learning for fine-grained visual categorization. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pp. 8241\u20138250. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00833","DOI":"10.1109\/ICCV.2019.00833"},{"issue":"1","key":"1228_CR18","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s11063-020-10246-3","volume":"52","author":"SD Meena","year":"2020","unstructured":"Meena, S.D., Agilandeeswari, L.: A new supervised clustering framework using multi discriminative parts and expectation-maximization approach for a fine-grained animal breed classification (SC-MPEM). Neural Process. Lett. 52(1), 727\u2013766 (2020). https:\/\/doi.org\/10.1007\/s11063-020-10246-3","journal-title":"Neural Process. Lett."},{"key":"1228_CR19","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1109\/LSP.2021.3114622","volume":"28","author":"Z Miao","year":"2021","unstructured":"Miao, Z., Zhao, X., Wang, J., Li, Y., Li, H.: Complemental attention multi-feature fusion network for fine-grained classification. IEEE Signal Process. Lett. 28, 1983\u20131987 (2021). https:\/\/doi.org\/10.1109\/LSP.2021.3114622","journal-title":"IEEE Signal Process. Lett."},{"key":"1228_CR20","doi-asserted-by":"publisher","first-page":"4996","DOI":"10.1109\/TIP.2020.2977457","volume":"29","author":"S Min","year":"2020","unstructured":"Min, S., Yao, H., Xie, H., Zha, Z., Zhang, Y.: Multi-objective matrix normalization for fine-grained visual recognition. IEEE Trans. Image Process. 29, 4996\u20135009 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2977457","journal-title":"IEEE Trans. Image Process."},{"key":"1228_CR21","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help? In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8\u201314 December 2019, pp. 4696\u20134705. Canada, Vancouver, BC (2019)"},{"key":"1228_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3168581","volume":"19","author":"Y Nie","year":"2022","unstructured":"Nie, Y., Bian, C., Li, L.: Adap-emd: Adaptive EMD for aircraft fine-grained classification in remote sensing. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2022.3168581","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"1228_CR23","doi-asserted-by":"publisher","first-page":"78503","DOI":"10.1109\/ACCESS.2018.2885055","volume":"6","author":"C Qiu","year":"2018","unstructured":"Qiu, C., Zhang, S., Wang, C., Yu, Z., Zheng, H., Zheng, B.: Improving transfer learning and squeeze- and-excitation networks for small-scale fine-grained fish image classification. IEEE Access 6, 78503\u201378512 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2885055","journal-title":"IEEE Access"},{"key":"1228_CR24","doi-asserted-by":"publisher","unstructured":"Rao, Y., Chen, G., Lu, J., Zhou, J.: Counterfactual attention learning for fine-grained visual categorization and re-identification. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pp. 1005\u20131014. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00106","DOI":"10.1109\/ICCV48922.2021.00106"},{"key":"1228_CR25","doi-asserted-by":"publisher","first-page":"108257","DOI":"10.1016\/j.patcog.2021.108257","volume":"121","author":"B Santra","year":"2022","unstructured":"Santra, B., Shaw, A., Mukherjee, D.P.: Part-based annotation-free fine-grained classification of images of retail products. Pattern Recognit. 121, 108257 (2022). https:\/\/doi.org\/10.1016\/j.patcog.2021.108257","journal-title":"Pattern Recognit."},{"key":"1228_CR26","doi-asserted-by":"publisher","unstructured":"Sun, M., Yuan, Y., Zhou, F., Ding, E.: Multi-attention multi-class constraint for fine-grained image recognition. In: V.\u00a0Ferrari, M.\u00a0Hebert, C.\u00a0Sminchisescu, Y.\u00a0Weiss (eds.) Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XVI, Lecture Notes in Computer Science, vol. 11220, pp. 834\u2013850. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_49","DOI":"10.1007\/978-3-030-01270-0_49"},{"key":"1228_CR27","doi-asserted-by":"publisher","first-page":"166390","DOI":"10.1109\/ACCESS.2019.2953957","volume":"7","author":"J Wang","year":"2019","unstructured":"Wang, J., Li, Y., Miao, Z., Zhao, X., Zhang, R.: Multi-level metric learning network for fine-grained classification. IEEE Access 7, 166390\u2013166397 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2953957","journal-title":"IEEE Access"},{"key":"1228_CR28","doi-asserted-by":"publisher","unstructured":"Wang, J., Li, Y., Wei, X., Li, H., Miao, Z., Zhang, R.: Bridge the gap between supervised and unsupervised learning for fine-grained classification. CoRR abs\/2203.00441 (2022). https:\/\/doi.org\/10.48550\/arXiv.2203.00441","DOI":"10.48550\/arXiv.2203.00441"},{"key":"1228_CR29","doi-asserted-by":"publisher","unstructured":"Wang, Y., Morariu, V.I., Davis, L.S.: Learning a discriminative filter bank within a CNN for fine-grained recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 4148\u20134157. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00436","DOI":"10.1109\/CVPR.2018.00436"},{"key":"1228_CR30","doi-asserted-by":"publisher","unstructured":"Wang, Z., Wang, S., Yang, S., Li, H., Li, J., Li, Z.: Weakly supervised fine-grained image classification via guassian mixture model oriented discriminative learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pp. 9746\u20139755. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00977","DOI":"10.1109\/CVPR42600.2020.00977"},{"key":"1228_CR31","doi-asserted-by":"publisher","unstructured":"Wei, X., Song, Y., Aodha, O.M., Wu, J., Peng, Y., Tang, J., Yang, J., Belongie, S.J.: Fine-grained image analysis with deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3126648","DOI":"10.1109\/TPAMI.2021.3126648"},{"key":"1228_CR32","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/j.patcog.2017.10.002","volume":"76","author":"X Wei","year":"2018","unstructured":"Wei, X., Xie, C., Wu, J., Shen, C.: Mask-cnn: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognit. 76, 704\u2013714 (2018). https:\/\/doi.org\/10.1016\/j.patcog.2017.10.002","journal-title":"Pattern Recognit."},{"key":"1228_CR33","doi-asserted-by":"publisher","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: B.\u00a0Leibe, J.\u00a0Matas, N.\u00a0Sebe, M.\u00a0Welling (eds.) Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VII, Lecture Notes in Computer Science, vol. 9911, pp. 499\u2013515. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"1228_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3162195","volume":"60","author":"W Xiong","year":"2022","unstructured":"Xiong, W., Xiong, Z., Cui, Y.: An explainable attention network for fine-grained ship classification using remote-sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022). https:\/\/doi.org\/10.1109\/TGRS.2022.3162195","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1228_CR35","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: D.N. Metaxas, L.\u00a0Quan, A.\u00a0Sanfeliu, L.V. Gool (eds.) IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011, pp. 2018\u20132025. IEEE Computer Society (2011). https:\/\/doi.org\/10.1109\/ICCV.2011.6126474","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"1228_CR36","doi-asserted-by":"publisher","unstructured":"Zhang, F., Li, M., Zhai, G., Liu, Y.: Multi-branch and multi-scale attention learning for fine-grained visual categorization. In: J.\u00a0Lokoc, T.\u00a0Skopal, K.\u00a0Schoeffmann, V.\u00a0Mezaris, X.\u00a0Li, S.\u00a0Vrochidis, I.\u00a0Patras (eds.) MultiMedia Modeling - 27th International Conference, MMM 2021, Prague, Czech Republic, June 22-24, 2021, Proceedings, Part I, Lecture Notes in Computer Science, vol. 12572, pp. 136\u2013147. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-67832-6_12","DOI":"10.1007\/978-3-030-67832-6_12"},{"key":"1228_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, H., Xu, T., Elhoseiny, M., Huang, X., Zhang, S., Elgammal, A.M., Metaxas, D.N.: SPDA-CNN: unifying semantic part detection and abstraction for fine-grained recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp. 1143\u20131152. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.129","DOI":"10.1109\/CVPR.2016.129"},{"key":"1228_CR38","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yan, K., Huang, F., Li, J.: Graph-based high-order relation discovery for fine-grained recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, pp. 15079\u201315088. Computer Vision Foundation \/ IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01483"},{"key":"1228_CR39","doi-asserted-by":"publisher","unstructured":"Zheng, H., Fu, J., Zha, Z., Luo, J.: Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 5012\u20135021. Computer Vision Foundation \/ IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00515","DOI":"10.1109\/CVPR.2019.00515"},{"key":"1228_CR40","doi-asserted-by":"crossref","unstructured":"Zhuang, P., Wang, Y., Qiao, Y.: Learning attentive pairwise interaction for fine-grained classification. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 13130\u201313137. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i07.7016"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-022-01228-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-022-01228-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-022-01228-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T12:12:46Z","timestamp":1663157566000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-022-01228-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,30]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["1228"],"URL":"https:\/\/doi.org\/10.1007\/s11554-022-01228-w","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2022,6,30]]},"assertion":[{"value":"18 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}