{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T02:30:44Z","timestamp":1782268244572,"version":"3.54.5"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"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":["Vis Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00371-025-03814-y","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T08:52:09Z","timestamp":1740732729000},"page":"7417-7432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Alleviating category confusion in fine-grained visual classification"],"prefix":"10.1007","volume":"41","author":[{"given":"Die","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyan","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"3814_CR1","doi-asserted-by":"crossref","unstructured":"Berg,T., Belhumeur,Peter\u00a0N: Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation. In: Proceed. IEEE Conf. Comput. Vision Pattern Recognition, 955\u2013962, (2013)","DOI":"10.1109\/CVPR.2013.128"},{"issue":"3","key":"3814_CR2","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1109\/TCSVT.2016.2617332","volume":"28","author":"Jianjun Lei","year":"2016","unstructured":"Lei, Jianjun, Duan, Jinhui, Feng, Wu., Ling, Nam, Hou, Chunping: Fast mode decision based on grayscale similarity and inter-view correlation for depth map coding in 3d-hevc. IEEE Trans. Circuits Syst. Video Technol. 28(3), 706\u2013718 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3814_CR3","doi-asserted-by":"crossref","unstructured":"Xie, L., Tian, Q., Hong, R., Yan, S., Zhang, B.: Hierarchical part matching for fine-grained visual categorization. In: Proceed. IEEE Int. Conf. Comput. Vision, pp. 1641\u20131648, (2013)","DOI":"10.1109\/ICCV.2013.206"},{"key":"3814_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based r-cnns for fine-grained category detection. In: Computer Vision\u2013ECCV 2014: 13th European Conf., Zurich, Switzerland, September 6-12, 2014, Proceed., Part I 13, pp. 834\u2013849. Springer, (2014)","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"3814_CR5","doi-asserted-by":"crossref","unstructured":"Huang, S., Xu, Z., Tao, D., Zhang, Y.: Part-stacked cnn for fine-grained visual categorization. In: Proceed. IEEE Conf. Comput Vision Pattern Recognition, pp. 1173\u20131182, (2016)","DOI":"10.1109\/CVPR.2016.132"},{"key":"3814_CR6","doi-asserted-by":"crossref","unstructured":"Fu, J., Zheng, H., Mei, T.: Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In: Proceed. IEEE conf. Comput. Vision Pattern Recognition, pp. 4438\u20134446, (2017)","DOI":"10.1109\/CVPR.2017.476"},{"key":"3814_CR7","doi-asserted-by":"crossref","unstructured":"Simonelli, A., Natale,F. De, Messelodi, S., Bulo, S.: Increasingly specialized ensemble of convolutional neural networks for fine-grained recognition. In: 2018 25th IEEE Int. Conf Image Process.(ICIP), 594\u2013598. IEEE, (2018)","DOI":"10.1109\/ICIP.2018.8451097"},{"key":"3814_CR8","unstructured":"Hu, T., Qi, H., Huang, Q., Lu, Y.: See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891, (2019)"},{"key":"3814_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, L., Huang, S., Liu, W., Tao, D.: Learning a mixture of granularity-specific experts for fine-grained categorization. In: Proceed. IEEE\/CVF Int. Conf. Comput. Vision, pp. 8331\u20138340, (2019)","DOI":"10.1109\/ICCV.2019.00842"},{"key":"3814_CR10","doi-asserted-by":"crossref","unstructured":"Hanselmann, H., Ney, H.: Elope: Fine-grained visual classification with efficient localization, pooling and embedding. In: Proceed. IEEE\/CVF winter Conf. Appl. Comput Vision, pp. 1247\u20131256, (2020)","DOI":"10.1109\/WACV45572.2020.9093601"},{"key":"3814_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, F., Li, M., Zhai, G., Liu, Y.: Multi-branch and multi-scale attention learning for fine-grained visual categorization. In: MultiMed. model.: 27th Int. Conf., MMM 2021, Prague, Czech Republic, June 22\u201324, 2021, Proceed., Part I 27, pp. 136\u2013147. Springer, (2021)","DOI":"10.1007\/978-3-030-67832-6_12"},{"key":"3814_CR12","unstructured":"Song, J., Yang, R.: Learning granularity-aware convolutional neural network for fine-grained visual classification. arXiv preprint arXiv:2103.02788, (2021)"},{"key":"3814_CR13","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TIP.2021.3135477","volume":"31","author":"Man Liu","year":"2021","unstructured":"Liu, Man, Zhang, Chunjie, Bai, Huihui, Zhang, Riquan, Zhao, Yao: Cross-part learning for fine-grained image classification. IEEE Trans. Image Process. 31, 748\u2013758 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3814_CR14","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset. (2011)"},{"key":"3814_CR15","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhang, L., Cheng,M-M., Feng, J.: Strip pooling: rethinking spatial pooling for scene parsing. In: Proceed. IEEE\/CVF conf. comput. vision pattern recognition, pp. 4003\u20134012, (2020)","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"3814_CR16","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceed. IEEE\/CVF conf. comput. vision pattern recognition, pp. 13713\u201313722, (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"3814_CR17","doi-asserted-by":"crossref","unstructured":"Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., Wang, L.: Learning to navigate for fine-grained classification. In: Proceed. European conf. comput. vision (ECCV), 420\u2013435, (2018)","DOI":"10.1007\/978-3-030-01264-9_26"},{"key":"3814_CR18","doi-asserted-by":"crossref","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: Proceed. IEEE\/CVF conf. comput. vision pattern recognition, 9749\u20139758, (2020)","DOI":"10.1109\/CVPR42600.2020.00977"},{"key":"3814_CR19","unstructured":"Yang, S., Liu, S., Yang, C., Wang, C.: Re-rank coarse classification with local region enhanced features for fine-grained image recognition. arXiv preprint arXiv:2102.09875, (2021)"},{"key":"3814_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Chuanbin, Xie, Hongtao, Zha, Zheng-Jun., Ma, Lingfeng, Lingyun, Yu., Zhang, Yongdong: Filtration and distillation: enhancing region attention for fine-grained visual categorization. In: Proceed. AAAI conf. artificial intelligence 34, 11555\u201311562 (2020)","DOI":"10.1609\/aaai.v34i07.6822"},{"key":"3814_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceed. IEEE conf. comput. vision pattern recognition, pp. 2921\u20132929, (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"3814_CR22","doi-asserted-by":"crossref","unstructured":"Selvaraju, R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceed. IEEE int. conf. comput vision, pp. 618\u2013626, (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"3814_CR23","doi-asserted-by":"crossref","unstructured":"Ge, W., Lin, X., Yu, Y.: Weakly supervised complementary parts models for fine-grained image classification from the bottom up. In: Proceed. IEEE\/CVF Conf. Comput. Vision Pattern Recognition, pp. 3034\u20133043, (2019)","DOI":"10.1109\/CVPR.2019.00315"},{"key":"3814_CR24","doi-asserted-by":"crossref","unstructured":"Zheng, H., Fu, J., Zha, Z-J., Luo, J.: Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition. In: Proceed. IEEE\/CVF conf. comput. vision pattern recognition, pp. 5012\u20135021, (2019)","DOI":"10.1109\/CVPR.2019.00515"},{"key":"3814_CR25","doi-asserted-by":"crossref","unstructured":"Shen, L., Hou, B., Jian, Y., Tu, X., Zhang, Y., Shuai, L., Ge, F., Chen, D.: Transfgvc: transformer-based fine-grained visual classification. Visual Comput., pp. 1\u201321, (2024)","DOI":"10.1007\/s00371-024-03545-6"},{"key":"3814_CR26","doi-asserted-by":"crossref","unstructured":"Graves, A., Graves, A.: Long short-term memory. Supervised sequence labelling with recurrent neural networks, pp. 37\u201345, (2012)","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"3814_CR27","doi-asserted-by":"crossref","unstructured":"Yang, S., Jin, Y., Lei, J., Zhang, S.: Multi-directional guidance network for fine-grained visual classification. Visual Computer, pp 1\u201312, (2024)","DOI":"10.1007\/s00371-023-03226-w"},{"key":"3814_CR28","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: NeurIPS, pp. 91\u201399, (2015)"},{"key":"3814_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117\u20132125, (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"3814_CR30","doi-asserted-by":"crossref","unstructured":"Ding, Y., Zhou, Y., Zhu, Y., Ye, Q., Jiao, J.: Selective sparse sampling for fine-grained image recognition. In: Proceed. IEEE\/CVF int. conf. comput. vision, pp. 6599\u20136608, (2019)","DOI":"10.1109\/ICCV.2019.00670"},{"key":"3814_CR31","doi-asserted-by":"crossref","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: Proceed. IEEE\/CVF Conf. Comput Vision Pattern Recognition, pp. 10468\u201310477, (2020)","DOI":"10.1109\/CVPR42600.2020.01048"},{"key":"3814_CR32","doi-asserted-by":"publisher","first-page":"104985","DOI":"10.1109\/ACCESS.2020.2999722","volume":"8","author":"Hua Wei","year":"2020","unstructured":"Wei, Hua, Zhu, Ming, Wang, Bo., Wang, Jiarong, Sun, Deyao: Two-level progressive attention convolutional network for fine-grained image recognition. IEEE Access 8, 104985\u2013104995 (2020)","journal-title":"IEEE Access"},{"key":"3814_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, T., Chang, D., Ma, Z., Guo, J.: Progressive co-attention network for fine-grained visual classification. In: 2021 Int. Conf. Visual Commun. Image Process. (VCIP), pp. 1\u20135. IEEE, (2021)","DOI":"10.1109\/VCIP53242.2021.9675376"},{"key":"3814_CR34","doi-asserted-by":"crossref","unstructured":"Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: Proceed. IEEE int. conf. comput. vision, pp. 5209\u20135217, (2017)","DOI":"10.1109\/ICCV.2017.557"},{"key":"3814_CR35","doi-asserted-by":"crossref","unstructured":"Sun, M., Yuan, Y., Zhou, F., Ding, E.: Multi-attention multi-class constraint for fine-grained image recognition. In: Proceed. European conf. comput. vision (ECCV), pp. 805\u2013821, (2018)","DOI":"10.1007\/978-3-030-01270-0_49"},{"key":"3814_CR36","doi-asserted-by":"crossref","unstructured":"Sun, G., Cholakkal, H., Khan, S., Khan, F., Shao, L.: Fine-grained recognition: accounting for subtle differences between similar classes. In: Proceed. AAAI conf artificial intelligence 34, 12047\u201312054 (2020)","DOI":"10.1609\/aaai.v34i07.6882"},{"key":"3814_CR37","doi-asserted-by":"crossref","unstructured":"Gao, Y., Han, X., Wang, X., Huang, W., Scott, M.: Channel interaction networks for fine-grained image categorization. In: Proceed. AAAI conf. artificial intelligence 34, 10818\u201310825 (2020)","DOI":"10.1609\/aaai.v34i07.6712"},{"key":"3814_CR38","doi-asserted-by":"crossref","unstructured":"Song, J., Yang, R.: Feature boosting, suppression, and diversification for fine-grained visual classification. In: 2021 Int. joint conf. neural networks (IJCNN), pp. 1\u20138. IEEE, 2021","DOI":"10.1109\/IJCNN52387.2021.9534004"},{"key":"3814_CR39","unstructured":"Xu, Q., Li, S., Wang, J., Jiang, B., Tang, J.: Context-semantic quality awareness network for fine-grained visual categorization. arXiv preprint arXiv:2403.10298, (2024)"},{"key":"3814_CR40","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Comput. vision\u2013ECCV 2016: 14th European conf., amsterdam, netherlands, October 11\u201314, 2016, proceed., part VII 14, pp. 499\u2013515. Springer, (2016)","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"3814_CR41","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L: Sphereface: Deep hypersphere embedding for face recognition. In: Proceed. IEEE conf. comput. vision pattern recognition, pp. 212\u2013220, (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"3814_CR42","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W.: Cosface: Large margin cosine loss for deep face recognition. In: Proceed. IEEE conf. comput. vision pattern recognition, pp. 5265\u20135274, (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"3814_CR43","doi-asserted-by":"crossref","unstructured":"Sun, G., Cholakkal, H., Khan, S., Khan, F., Shao, L.: Fine-grained recognition: accounting for subtle differences between similar classes. In: Proceed. AAAI conf. on artificial intelligence 34, 12047\u201312054 (2020)","DOI":"10.1609\/aaai.v34i07.6882"},{"key":"3814_CR44","doi-asserted-by":"crossref","unstructured":"Chang, D., Ding, Y., Xie,J., Kumar Bhunia, A., Li, X., Ma, Z., Wu, M., Guo, J., Song, Y-Z.:Mutual-channel loss for fine-grained image classification. The devil is in the channels. IEEE Trans. Image Process.29, 4683\u20134695 (2020)","DOI":"10.1109\/TIP.2020.2973812"},{"key":"3814_CR45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp: 770\u2013778, (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3814_CR46","doi-asserted-by":"crossref","unstructured":"Choe, J., Shim, H.: Attention-based dropout layer for weakly supervised object localization. In: Proceed. IEEE\/CVF conf. comput. vision pattern recognition, pp. 2219\u20132228, (2019)","DOI":"10.1109\/CVPR.2019.00232"},{"issue":"12","key":"3814_CR47","first-page":"9521","volume":"44","author":"Du Ruoyi","year":"2021","unstructured":"Ruoyi, Du., Xie, Jiyang, Ma, Zhanyu, Chang, Dongliang, Song, Yi-Zhe., Guo, Jun: Progressive learning of category-consistent multi-granularity features for fine-grained visual classification. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 9521\u20139535 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3814_CR48","doi-asserted-by":"crossref","unstructured":"Du, R., Chang, D., Bhunia, A., Xie, J, Ma., Z., Song, Y-Z., Guo, J.: Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. In: European Conf. Comput. Vision, pp. 153\u2013168. Springer, (2020)","DOI":"10.1007\/978-3-030-58565-5_10"},{"key":"3814_CR49","unstructured":"Do, T., Tran, H., Tjiputra, E., Tran, Q., Nguyen, A.: Fine-grained visual classification using self assessment classifier. arXiv preprint arXiv:2205.10529, (2022)"},{"key":"3814_CR50","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, (2015)"},{"key":"3814_CR51","first-page":"14068","volume":"33","author":"Chaoyang He","year":"2020","unstructured":"He, Chaoyang, Annavaram, Murali, Avestimehr, Salman: Group knowledge transfer: federated learning of large cnns at the edge. Adv. Neural. Inf. Process. Syst. 33, 14068\u201314080 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3814_CR52","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceed. IEEE\/CVF conf. comput. vision pattern recognition, pp. 2495\u20132504, (2021)","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"3814_CR53","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceed. IEEE int. conf. comput. vision workshops, pp. 554\u2013561, (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"3814_CR54","unstructured":"Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, (2013)"},{"key":"3814_CR55","doi-asserted-by":"crossref","unstructured":"Zhuang, P., Wang, Y., Qiao, Y.: Learning attentive pairwise interaction for fine-grained classification. In: Proceed. AAAI conf. artificial intelligence 34, 13130\u201313137 (2020)","DOI":"10.1609\/aaai.v34i07.7016"},{"key":"3814_CR56","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1109\/TIP.2021.3055617","volume":"30","author":"Yifeng Ding","year":"2021","unstructured":"Ding, Yifeng, Ma, Zhanyu, Wen, Shaoguo, Xie, Jiyang, Chang, Dongliang, Si, Zhongwei, Ming, Wu., Ling, Haibin: Ap-cnn: weakly supervised attention pyramid convolutional neural network for fine-grained visual classification. IEEE Trans. Image Process. 30, 2826\u20132836 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3814_CR57","doi-asserted-by":"crossref","unstructured":"Huang, S., Wang, X., Tao, D.: Stochastic partial swap: Enhanced model generalization and interpretability for fine-grained recognition. In: Proceed. IEEE\/CVF Int. Conf. Comput. Vision, pp. 620\u2013629, (2021)","DOI":"10.1109\/ICCV48922.2021.00066"},{"key":"3814_CR58","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, H., Wang, Z., Ouyang, W.: Dynamic position-aware network for fine-grained image recognition. In: Proceed. AAAI Conf. Artificial Intelligence 35, 2791\u20132799 (2021)","DOI":"10.1609\/aaai.v35i4.16384"},{"key":"3814_CR59","doi-asserted-by":"publisher","first-page":"9470","DOI":"10.1109\/TIP.2021.3126490","volume":"30","author":"Yifan Zhao","year":"2021","unstructured":"Zhao, Yifan, Li, Jia, Chen, Xiaowu, Tian, Yonghong: Part-guided relational transformers for fine-grained visual recognition. IEEE Trans. Image Process. 30, 9470\u20139481 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"3814_CR60","doi-asserted-by":"publisher","first-page":"4409","DOI":"10.1109\/TMM.2021.3117064","volume":"24","author":"Lianbo Zhang","year":"2022","unstructured":"Zhang, Lianbo, Huang, Shaoli, Liu, Wei: Enhancing mixture-of-experts by leveraging attention for fine-grained recognition. IEEE Trans. Multimed. 24, 4409\u20134421 (2022)","journal-title":"IEEE Trans. Multimed."},{"issue":"3","key":"3814_CR61","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1109\/TCSVT.2021.3069835","volume":"32","author":"Yao Ding","year":"2022","unstructured":"Ding, Yao, Han, Zhenjun, Zhou, Yanzhao, Zhu, Yi., Chen, Jie, Ye, Qixiang, Jiao, Jianbin: Dynamic perception framework for fine-grained recognition. IEEE Trans. Circuits Syst. Video Technol. 32(3), 1353\u20131365 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3814_CR62","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, Y., Chen, K., Xu, Y., Tian, Y.: Fine-grained object classification via self-supervised pose alignment. In: Proceed. IEEE\/CVF Conf. Comput. Vision Pattern Recognition, pp. 7399\u20137408, (2022)","DOI":"10.1109\/CVPR52688.2022.00725"},{"key":"3814_CR63","doi-asserted-by":"publisher","first-page":"4186","DOI":"10.1109\/TIP.2022.3181492","volume":"31","author":"Weijian Deng","year":"2022","unstructured":"Deng, Weijian, Marsh, Joshua, Gould, Stephen, Zheng, Liang: Fine-grained classification via categorical memory networks. IEEE Trans. Image Process. 31, 4186\u20134196 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"3814_CR64","unstructured":"Liang, Y., Zhu, L., Wang, X., Yang, Y.: Penalizing the hard example but not too much: a strong baseline for fine-grained visual classification. IEEE Trans Neural Networks Learn. Syst. (2022)"},{"key":"3814_CR65","doi-asserted-by":"publisher","first-page":"109305","DOI":"10.1016\/j.patcog.2023.109305","volume":"137","author":"Xiao Ke","year":"2023","unstructured":"Ke, Xiao, Cai, Yuhang, Chen, Baitao, Liu, Hao, Guo, Wenzhong: Granularity-aware distillation and structure modeling region proposal network for fine-grained image classification. Pattern Recogn. 137, 109305 (2023)","journal-title":"Pattern Recogn."},{"key":"3814_CR66","doi-asserted-by":"crossref","unstructured":"Guan, X., Yang, Y., Li, J., Zhu, Xi., Song, J., Shen, H.\u00a0Tao: On the imaginary wings: Text-assisted complex-valued fusion network for fine-grained visual classification. IEEE Trans. Neural Networks Learn. Syst., 34(8):5112\u20135121, (2021)","DOI":"10.1109\/TNNLS.2021.3126046"},{"key":"3814_CR67","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neunet.2023.01.050","volume":"161","author":"Peng Zhao","year":"2023","unstructured":"Zhao, Peng, Li, Yi., Tang, Baowei, Liu, Huiting, Yao, Sheng: Feature relocation network for fine-grained image classification. Neural Netw. 161, 306\u2013317 (2023)","journal-title":"Neural Netw."},{"key":"3814_CR68","unstructured":"Zheng, H., Fu, J., Zha, Z-J., Luo, J.: Learning deep bilinear transformation for fine-grained image representation. Adv. Neural Inform. Process. Syst. (2019)"},{"key":"3814_CR69","doi-asserted-by":"crossref","unstructured":"Rao, Y., Chen, G., Lu, J., Zhou, J.: Counterfactual attention learning for fine-grained visual categorization and re-identification. In: Proceed. IEEE\/CVF int. conf. comput. vision, pp. 1025\u20131034, (2021)","DOI":"10.1109\/ICCV48922.2021.00106"},{"key":"3814_CR70","doi-asserted-by":"crossref","unstructured":"Hu, Y., Liu, X., Zhang, B., Han, J., Cao, X.: Alignment enhancement network for fine-grained<? brk?> visual categorization. ACM Trans. Multimed. Comput., Commun., Appli. (TOMM), 17(1s):1\u201320, (2021)","DOI":"10.1145\/3446208"},{"key":"3814_CR71","doi-asserted-by":"publisher","first-page":"108219","DOI":"10.1016\/j.patcog.2021.108219","volume":"121","author":"Lianbo Zhang","year":"2022","unstructured":"Zhang, Lianbo, Huang, Shaoli, Liu, Wei: Learning sequentially diversified representations for fine-grained categorization. Pattern Recogn. 121, 108219 (2022)","journal-title":"Pattern Recogn."},{"key":"3814_CR72","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J-Y., Kweon,I.: Cbam: Convolutional block attention module. In: Proceed. European conf. comput. vision (ECCV), pp. 3\u201319, (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3814_CR73","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceed. IEEE conf. comput. vision pattern recognition, pp. 7132\u20137141, (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3814_CR74","doi-asserted-by":"crossref","unstructured":"Yu, D., Fang, Z., Jiang, Y.: Foreground feature enhancement and peak & background suppression for fine-grained visual classification. In: Int. Conf. Multimed. Model., pp. 134\u2013146. Springer, (2024)","DOI":"10.1007\/978-3-031-53305-1_11"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03814-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-03814-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03814-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T07:00:42Z","timestamp":1757142042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-03814-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,27]]},"references-count":74,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["3814"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-03814-y","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,27]]},"assertion":[{"value":"14 January 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}