{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T09:34:32Z","timestamp":1768901672034,"version":"3.49.0"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176093"],"award-info":[{"award-number":["62176093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s11263-024-02288-0","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T13:06:35Z","timestamp":1732539995000},"page":"2441-2462","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Globally Correlation-Aware Hard Negative Generation"],"prefix":"10.1007","volume":"133","author":[{"given":"Wenjie","family":"Peng","sequence":"first","affiliation":[]},{"given":"Hongxiang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Tianshui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Quhui","family":"Ke","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Dai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5544-4544","authenticated-orcid":false,"given":"Shuangping","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"2288_CR1","doi-asserted-by":"crossref","unstructured":"Aziere, N., & Todorovic, S. (2019). Ensemble deep manifold similarity learning using hard proxies. In CVPR (pp. 7299\u20137307).","DOI":"10.1109\/CVPR.2019.00747"},{"key":"2288_CR2","doi-asserted-by":"crossref","unstructured":"Bucher, M., Herbin, S., & Jurie, F. (2016). Hard negative mining for metric learning based zero-shot classification. In ECCV (pp. 524\u2013531). Springer.","DOI":"10.1007\/978-3-319-49409-8_45"},{"key":"2288_CR3","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) Emerging properties in self-supervised vision transformers. In ICCV (pp. 9650\u20139660).","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"2288_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Lin, L., Chen, R., Hui, X., & Wu, H. (2022a). Knowledge-guided multi-label few-shot learning for general image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), 1371\u20131384.","DOI":"10.1109\/TPAMI.2020.3025814"},{"key":"2288_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Pu, T., Liu, L., Shi, Y., Yang, Z., & Lin, L. (2024a). Heterogeneous semantic transfer for multi-label recognition with partial labels. International Journal of Computer Vision, 132, 6091\u20136106.","DOI":"10.1007\/s11263-024-02127-2"},{"issue":"12","key":"2288_CR6","doi-asserted-by":"publisher","first-page":"9887","DOI":"10.1109\/TPAMI.2021.3131222","volume":"44","author":"T Chen","year":"2022","unstructured":"Chen, T., Pu, T., Wu, H., Xie, Y., Liu, L., & Lin, L. (2022b). Cross-domain facial expression recognition: A unified evaluation benchmark and adversarial graph learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9887\u20139903.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2288_CR7","doi-asserted-by":"publisher","first-page":"4811","DOI":"10.1109\/TIP.2024.3448248","volume":"33","author":"T Chen","year":"2024","unstructured":"Chen, T., Wang, W., Pu, T., Qin, J., Yang, Z., Liu, J., & Lin, L. (2024b). Dynamic correlation learning and regularization for multi-label confidence calibration. IEEE Transactions on Image Processing, 33, 4811\u20134823.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2288_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Z. M., Wei, X. S., Wang, P., & Guo, Y. (2019). Multi-label image recognition with graph convolutional networks. In CVPR (pp. 5177\u20135186).","DOI":"10.1109\/CVPR.2019.00532"},{"key":"2288_CR9","doi-asserted-by":"crossref","unstructured":"Dai, G., Zhang, Y., Wang, Q., Du, Q., Yu, Z., Liu, Z., & Huang, S. (2023). Disentangling writer and character styles for handwriting generation. In CVPR (pp. 5977\u20135986).","DOI":"10.1109\/CVPR52729.2023.00579"},{"key":"2288_CR10","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., & Gelly, S., et\u00a0al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929."},{"key":"2288_CR11","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1109\/TIP.2019.2948472","volume":"29","author":"Y Duan","year":"2019","unstructured":"Duan, Y., Lu, J., Zheng, W., & Zhou, J. (2019). Deep adversarial metric learning. IEEE Transactions on Image Processing, 29, 2037\u20132051.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"2","key":"2288_CR12","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1109\/TPAMI.2022.3163846","volume":"45","author":"I Elezi","year":"2022","unstructured":"Elezi, I., Seidenschwarz, J., Wagner, L., Vascon, S., Torcinovich, A., Pelillo, M., & Leal-Taixe, L. (2022). The group loss++: A deeper look into group loss for deep metric learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 2505\u20132518.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2288_CR13","doi-asserted-by":"crossref","unstructured":"Elezi, I., Vascon, S., Torcinovich, A., Pelillo, M., & Leal-Taix\u00e9, L. (2020). The group loss for deep metric learning. In ECCV (pp. 277\u2013294). Springer.","DOI":"10.1007\/978-3-030-58571-6_17"},{"key":"2288_CR14","doi-asserted-by":"crossref","unstructured":"Ermolov, A., Mirvakhabova, L., Khrulkov, V., Sebe, N., & Oseledets, I. (2022). Hyperbolic vision transformers: Combining improvements in metric learning. In CVPR (pp. 7409\u20137419).","DOI":"10.1109\/CVPR52688.2022.00726"},{"key":"2288_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107795","volume":"112","author":"B Gaji\u0107","year":"2021","unstructured":"Gaji\u0107, B., Amato, A., & Gatta, C. (2021). Fast hard negative mining for deep metric learning. Pattern Recognition, 112, 107795.","journal-title":"Pattern Recognition"},{"key":"2288_CR16","doi-asserted-by":"crossref","unstructured":"Gu, G., & Ko, B. (2020). Symmetrical synthesis for deep metric learning. In AAAI (pp. 10853\u201310860).","DOI":"10.1609\/aaai.v34i07.6716"},{"key":"2288_CR17","doi-asserted-by":"crossref","unstructured":"Gu, G., Ko, B., & Kim, H. G. (2021). Proxy synthesis: Learning with synthetic classes for deep metric learning. In AAAI (pp. 1460\u20131468).","DOI":"10.1609\/aaai.v35i2.16236"},{"key":"2288_CR18","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In CVPR (pp. 1735\u20131742).","DOI":"10.1109\/CVPR.2006.100"},{"key":"2288_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"2288_CR20","doi-asserted-by":"crossref","unstructured":"Huang, J., Feng, Y., Zhou, M., & Qiang, B. (2020). Relationship-aware hard negative generation in deep metric learning. In KSEM (pp. 388\u2013400). Springer.","DOI":"10.1007\/978-3-030-55393-7_35"},{"key":"2288_CR21","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1007\/s11263-021-01444-0","volume":"129","author":"SS Husain","year":"2021","unstructured":"Husain, S. S., Ong, E. J., & Bober, M. (2021). ACTNET: End-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval. International Journal of Computer Vision, 129, 1432\u20131450.","journal-title":"International Journal of Computer Vision"},{"key":"2288_CR22","unstructured":"Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML (pp. 448\u2013456). PMLR."},{"key":"2288_CR23","doi-asserted-by":"crossref","unstructured":"Jin, S., RoyChowdhury, A., Jiang, H., Singh, A., Prasad, A., Chakraborty, D., & Learned-Miller, E. (2018). Unsupervised hard example mining from videos for improved object detection. In ECCV (pp. 307\u2013324).","DOI":"10.1007\/978-3-030-01261-8_19"},{"key":"2288_CR24","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016). Molecular graph convolutions: Moving beyond fingerprints. Journal of Computer-Aided Molecular Design, 30, 595\u2013608.","journal-title":"Journal of Computer-Aided Molecular Design"},{"key":"2288_CR25","doi-asserted-by":"crossref","unstructured":"Kim, S., Jeong, B., & Kwak, S. (2023). HIER: Metric learning beyond class labels via hierarchical regularization. In CVPR (pp. 19903\u201319912).","DOI":"10.1109\/CVPR52729.2023.01906"},{"key":"2288_CR26","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, D., Cho, M., & Kwak, S. (2020). Proxy anchor loss for deep metric learning. In CVPR (pp. 3238\u20133247).","DOI":"10.1109\/CVPR42600.2020.00330"},{"key":"2288_CR27","unstructured":"Kipf, T.N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"2288_CR28","doi-asserted-by":"crossref","unstructured":"Ko, B., & Gu, G. (2020). Embedding expansion: Augmentation in embedding space for deep metric learning. In CVPR (pp. 7255\u20137264).","DOI":"10.1109\/CVPR42600.2020.00728"},{"key":"2288_CR29","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., & Fei-Fei, L. (2013). 3d object representations for fine-grained categorization. In ICCVW (pp. 554\u2013561).","DOI":"10.1109\/ICCVW.2013.77"},{"key":"2288_CR30","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, Z., Wang, J., Zhang, X., Ding, E., Wang, J., & Zhang, Z. (2022). Self-guided hard negative generation for unsupervised person re-identification. In IJCAI.","DOI":"10.24963\/ijcai.2022\/149"},{"key":"2288_CR31","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1007\/s11263-020-01331-0","volume":"128","author":"Z Li","year":"2020","unstructured":"Li, Z., Tang, J., Zhang, L., & Yang, J. (2020). Weakly-supervised semantic guided hashing for social image retrieval. International Journal of Computer Vision, 128, 2265\u20132278.","journal-title":"International Journal of Computer Vision"},{"key":"2288_CR32","doi-asserted-by":"crossref","unstructured":"Liao, S., & Shao, L. (2022). Graph sampling based deep metric learning for generalizable person re-identification. In CVPR (pp. 7359\u20137368).","DOI":"10.1109\/CVPR52688.2022.00721"},{"key":"2288_CR33","doi-asserted-by":"crossref","unstructured":"Lim, J., Yun, S., Park, S., & Choi, J. Y (2022) Hypergraph-induced semantic tuplet loss for deep metric learning. In CVPR (pp. 212\u2013222).","DOI":"10.1109\/CVPR52688.2022.00031"},{"issue":"8\u20139","key":"2288_CR34","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1007\/s11263-020-01315-0","volume":"128","author":"H Liu","year":"2020","unstructured":"Liu, H., Wang, R., Shan, S., & Chen, X. (2020). Learning multifunctional binary codes for personalized image retrieval. International Journal of Computer Vision, 128(8\u20139), 2223\u20132242.","journal-title":"International Journal of Computer Vision"},{"key":"2288_CR35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X. (2016). Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In CVPR (pp. 1096\u20131104).","DOI":"10.1109\/CVPR.2016.124"},{"key":"2288_CR36","unstructured":"Loshchilov, I., & Hutter, F. (2018). Decoupled weight decay regularization. In ICLR."},{"issue":"9","key":"2288_CR37","doi-asserted-by":"publisher","first-page":"4269","DOI":"10.1109\/TIP.2017.2717505","volume":"26","author":"J Lu","year":"2017","unstructured":"Lu, J., Hu, J., & Tan, Y. P. (2017). Discriminative deep metric learning for face and kinship verification. IEEE Transactions on Image Processing, 26(9), 4269\u20134282.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2288_CR38","doi-asserted-by":"crossref","unstructured":"Movshovitz-Attias, Y., Toshev, A., Leung, T. K., Ioffe, S., & Singh, S. (2017). No fuss distance metric learning using proxies. In ICCV (pp. 360\u2013368).","DOI":"10.1109\/ICCV.2017.47"},{"key":"2288_CR39","doi-asserted-by":"crossref","unstructured":"Musgrave, K., Belongie, S., & Lim, S. N. (2020). A metric learning reality check. In ECCV (pp. 681\u2013699). Springer.","DOI":"10.1007\/978-3-030-58595-2_41"},{"key":"2288_CR40","doi-asserted-by":"crossref","unstructured":"Oh\u00a0Song, H., Xiang, Y., Jegelka, S., & Savarese, S. (2016). Deep metric learning via lifted structured feature embedding. In CVPR (pp. 4004\u20134012).","DOI":"10.1109\/CVPR.2016.434"},{"key":"2288_CR41","doi-asserted-by":"crossref","unstructured":"Qian, Q., Shang, L., Sun, B., Hu, J., Li, H., & Jin, R. (2019). Softtriple loss: Deep metric learning without triplet sampling. In ICCV (pp. 6450\u20136458).","DOI":"10.1109\/ICCV.2019.00655"},{"key":"2288_CR42","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/s11263-023-01864-0","volume":"132","author":"H Rao","year":"2023","unstructured":"Rao, H., Leung, C., & Miao, C. (2023). Hierarchical skeleton meta-prototype contrastive learning with hard skeleton mining for unsupervised person re-identification. International Journal of Computer Vision, 132, 238\u2013260.","journal-title":"International Journal of Computer Vision"},{"key":"2288_CR43","doi-asserted-by":"crossref","unstructured":"Roth, K., Vinyals, O., & Akata, Z. (2022). Non-isotropy regularization for proxy-based deep metric learning. In CVPR (pp. 7420\u20137430).","DOI":"10.1109\/CVPR52688.2022.00727"},{"issue":"3","key":"2288_CR44","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211\u2013252.","journal-title":"International Journal of Computer Vision"},{"key":"2288_CR45","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. In CVPR (pp. 815\u2013823).","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"2288_CR46","unstructured":"Seidenschwarz, J.D., Elezi, I., & Leal-Taix\u00e9, L. (2021). Learning intra-batch connections for deep metric learning. In ICML (pp. 9410\u20139421). PMLR."},{"key":"2288_CR47","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., & Girshick, R. (2016). Training region-based object detectors with online hard example mining. In CVPR (pp. 761\u2013769).","DOI":"10.1109\/CVPR.2016.89"},{"key":"2288_CR48","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., & Moreno-Noguer, F. (2015). Discriminative learning of deep convolutional feature point descriptors. In ICCV (pp. 118\u2013126).","DOI":"10.1109\/ICCV.2015.22"},{"key":"2288_CR49","doi-asserted-by":"crossref","unstructured":"Smirnov, E., Melnikov, A., Oleinik, A., Ivanova, E., Kalinovskiy, I., & Luckyanets, E. (2018). Hard example mining with auxiliary embeddings. In CVPRW (pp. 37\u201346).","DOI":"10.1109\/CVPRW.2018.00013"},{"key":"2288_CR50","unstructured":"Sohn, K. (2016). Improved deep metric learning with multi-class n-pair loss objective. In NeurIPS (pp. 1857\u20131865)."},{"key":"2288_CR51","doi-asserted-by":"crossref","unstructured":"Suh, Y., Han, B., Kim, W., Lee, K. M. (2019). Stochastic class-based hard example mining for deep metric learning. In CVPR (pp. 7251\u20137259).","DOI":"10.1109\/CVPR.2019.00742"},{"key":"2288_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In CVPR (pp. 1\u20139).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2288_CR53","doi-asserted-by":"publisher","first-page":"3224","DOI":"10.1109\/TIP.2021.3137005","volume":"31","author":"Z Tan","year":"2022","unstructured":"Tan, Z., Liu, A., Wan, J., Liu, H., Lei, Z., Guo, G., & Li, S. Z. (2022). Cross-batch hard example mining with pseudo large batch for id vs. spot face recognition. IEEE Transactions on Image Processing, 31, 3224\u20133235.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2288_CR54","doi-asserted-by":"crossref","unstructured":"Teh, E. W., DeVries, T., & Taylor, G. W. (2020). ProxyNCA++: Revisiting and revitalizing proxy neighborhood component analysis. In ECCV (pp. 448\u2013464). Springer.","DOI":"10.1007\/978-3-030-58586-0_27"},{"key":"2288_CR55","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & J\u00e9gou, H. (2021). Training data-efficient image transformers & distillation through attention. In ICML (Vol. 10, pp. 347\u2013357). PMLR."},{"key":"2288_CR56","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., & Polosukhin, I. (2017). Attention is all you need. In NeurIPS (Vol. 30)."},{"key":"2288_CR57","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., & Bengio, Y. (2018). Graph attention networks. In ICLR."},{"key":"2288_CR58","unstructured":"Venkataramanan, S., Psomas, B., Kijak, E., Amsaleg, L., Karantzalos, K., & Avrithis, Y. (2022). It takes two to tango: Mixup for deep metric learning. In ICLR (pp. 1\u201321)."},{"key":"2288_CR59","doi-asserted-by":"crossref","unstructured":"Wang, C., Zheng, W., Li, J., Zhou, J., Lu, J. (2023). Deep factorized metric learning. In CVPR (pp. 7672\u20137682).","DOI":"10.1109\/CVPR52729.2023.00741"},{"key":"2288_CR60","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhou, F., Wen, S., Liu, X., & Lin, Y. (2017). Deep metric learning with angular loss. In ICCV (pp. 2593\u20132601).","DOI":"10.1109\/ICCV.2017.283"},{"key":"2288_CR61","doi-asserted-by":"crossref","unstructured":"Wang, X., Han, X., Huang, W., Dong, D., & Scott, M. R. (2019). Multi-similarity loss with general pair weighting for deep metric learning. In CVPR (pp. 5022\u20135030).","DOI":"10.1109\/CVPR.2019.00516"},{"key":"2288_CR62","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., & Wen, S. (2020). Multi-label classification with label graph superimposing. In AAAI (Vol.\u00a034, pp. 12265\u201312272).","DOI":"10.1609\/aaai.v34i07.6909"},{"issue":"2","key":"2288_CR63","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10(2), 207\u2013244.","journal-title":"Journal of Machine Learning Research"},{"key":"2288_CR64","unstructured":"Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., & Perona, P. (2010). Caltech-UCSD birds 200. California Institute of Technology."},{"key":"2288_CR65","doi-asserted-by":"crossref","unstructured":"Xuan, H., Stylianou, A., & Pless, R. (2020). Improved embeddings with easy positive triplet mining. In WACV (pp. 2474\u20132482).","DOI":"10.1109\/WACV45572.2020.9093432"},{"key":"2288_CR66","doi-asserted-by":"crossref","unstructured":"Yang, B., Sun, H., Li, F. W., Chen, Z., Cai, J., & Song, C. (2023). HSE: Hybrid species embedding for deep metric learning. In ICCV (Vol. 11, pp. 047\u2013057).","DOI":"10.1109\/ICCV51070.2023.01014"},{"key":"2288_CR67","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhan, X., Chen, D., Yan, J., Loy, C. C., & Lin, D. (2019). Learning to cluster faces on an affinity graph. In CVPR (pp. 2298\u20132306).","DOI":"10.1109\/CVPR.2019.00240"},{"key":"2288_CR68","doi-asserted-by":"crossref","unstructured":"Yang, Z., Bastan, M., Zhu, X., Gray, D., & Samaras, D. (2022). Hierarchical proxy-based loss for deep metric learning. In WACV (pp. 1859\u20131868).","DOI":"10.1109\/WACV51458.2022.00052"},{"key":"2288_CR69","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/s11263-020-01382-3","volume":"129","author":"Q Yu","year":"2021","unstructured":"Yu, Q., Song, J., Song, Y. Z., Xiang, T., & Hospedales, T. M. (2021). Fine-grained instance-level sketch-based image retrieval. International Journal of Computer Vision, 129, 484\u2013500.","journal-title":"International Journal of Computer Vision"},{"issue":"12","key":"2288_CR70","doi-asserted-by":"publisher","first-page":"10528","DOI":"10.1109\/TNNLS.2022.3168431","volume":"34","author":"Y Zeng","year":"2022","unstructured":"Zeng, Y., Wang, Y., Liao, D., Li, G., Huang, W., Xu, J., Cao, D., & Man, H. (2022). Keyword-based diverse image retrieval with variational multiple instance graph. IEEE Transactions on Neural Networks and Learning Systems, 34(12), 10528\u201310537.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2288_CR71","unstructured":"Zhai, A., & Wu, H. Y. (2018). Classification is a strong baseline for deep metric learning. arXiv preprint arXiv:1811.12649"},{"key":"2288_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, B., Zheng, W., Zhou, J., & Lu, J. (2022). Attributable visual similarity learning. In CVPR (pp. 7532\u20137541).","DOI":"10.1109\/CVPR52688.2022.00738"},{"key":"2288_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, C., Luo, L., & Gu, B. (2023). Denoising multi-similarity formulation: A self-paced curriculum-driven approach for robust metric learning. In AAAI (Vol. 37, pp. 11183\u201311191).","DOI":"10.1609\/aaai.v37i9.26324"},{"key":"2288_CR74","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Jin, Z., Qi, G., Lu, H., & Hua, X. (2018). An adversarial approach to hard triplet generation. In ECCV (pp. 501\u2013517).","DOI":"10.1007\/978-3-030-01240-3_31"},{"key":"2288_CR75","doi-asserted-by":"crossref","unstructured":"Zheng, W., Chen, Z., Lu, J., Zhou, J. (2019). Hardness-aware deep metric learning. In CVPR (pp. 72\u201381).","DOI":"10.1109\/CVPR.2019.00016"},{"issue":"9","key":"2288_CR76","doi-asserted-by":"publisher","first-page":"3214","DOI":"10.1109\/TPAMI.2020.2980231","volume":"43","author":"W Zheng","year":"2021","unstructured":"Zheng, W., Lu, J., & Zhou, J. (2021). Hardness-aware deep metric learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3214\u20133228.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2288_CR77","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neucom.2022.05.032","volume":"498","author":"C Zhu","year":"2022","unstructured":"Zhu, C., Hu, Z., Dong, H., He, G., Yu, Z., & Zhang, S. (2022). Construct informative triplet with two-stage hard-sample generation. Neurocomputing, 498, 59\u201374.","journal-title":"Neurocomputing"},{"issue":"11","key":"2288_CR78","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1007\/s11263-023-01841-7","volume":"131","author":"J Zhu","year":"2023","unstructured":"Zhu, J., Liu, L., Zhan, Y., Zhu, X., Zeng, H., & Tao, D. (2023). Attribute-image person re-identification via modal-consistent metric learning. International Journal of Computer Vision, 131(11), 2959\u20132976.","journal-title":"International Journal of Computer Vision"},{"key":"2288_CR79","doi-asserted-by":"publisher","first-page":"7593","DOI":"10.1109\/TIP.2021.3107214","volume":"30","author":"S Zhu","year":"2021","unstructured":"Zhu, S., Yang, T., & Chen, C. (2021). Visual explanation for deep metric learning. IEEE Transactions on Image Processing, 30, 7593\u20137607.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2288_CR80","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2019.06.007","volume":"95","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Jing, X. Y., Zhang, F., Zhang, X., You, X., & Cui, X. (2019). Distance learning by mining hard and easy negative samples for person re-identification. Pattern Recognition, 95, 211\u2013222.","journal-title":"Pattern Recognition"},{"key":"2288_CR81","unstructured":"Zhu, Y., Yang, M., Deng, C., & Liu, W. (2020). Fewer is more: A deep graph metric learning perspective using fewer proxies. In NeurIPS (Vol. 17, pp. 792\u2013803)."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02288-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02288-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02288-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T05:59:45Z","timestamp":1744869585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02288-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":81,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["2288"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02288-0","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,25]]},"assertion":[{"value":"6 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The codes and trained models are publicly available on GitHub: .","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}]}}