{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T19:10:08Z","timestamp":1745608208961,"version":"3.40.4"},"publisher-location":"Singapore","reference-count":51,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819658145","type":"print"},{"value":"9789819658152","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-5815-2_19","type":"book-chapter","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:43:14Z","timestamp":1745606594000},"page":"373-398","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Trademark Retrieval Method Based on\u00a0Self-supervised Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3796-918X","authenticated-orcid":false,"given":"Kailang","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4664-419X","authenticated-orcid":false,"given":"Yixiao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2717-9170","authenticated-orcid":false,"given":"Huibing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9455-3850","authenticated-orcid":false,"given":"Xuan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Aker, C., Tursun, O., Kalkan, S.: Analyzing deep features for trademark retrieval. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp.\u00a01\u20134. IEEE (2017)","DOI":"10.1109\/SIU.2017.7960426"},{"key":"19_CR2","unstructured":"Babenko, A., Lempitsky, V.: Aggregating local deep features for image retrieval. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1269\u20131277 (2015)"},{"key":"19_CR3","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: Vicreg: variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906 (2021)"},{"issue":"5","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.3390\/s21051894","volume":"21","author":"J Cao","year":"2021","unstructured":"Cao, J., Huang, Y., Dai, Q., Ling, W.K.: Unsupervised trademark retrieval method based on attention mechanism. Sensors 21(5), 1894 (2021)","journal-title":"Sensors"},{"key":"19_CR5","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems, vol. 33, pp. 9912\u20139924 (2020)"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Chatfield, K., Philbin, J., Zisserman, A.: Efficient retrieval of deformable shape classes using local self-similarities. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 264\u2013271. IEEE (2009)","DOI":"10.1109\/ICCVW.2009.5457691"},{"key":"19_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"19_CR8","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol.\u00a01, pp. 539\u2013546. IEEE (2005)","DOI":"10.1109\/CVPR.2005.202"},{"issue":"8","key":"19_CR11","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1016\/S0031-3203(00)00055-8","volume":"34","author":"G Ciocca","year":"2001","unstructured":"Ciocca, G., Schettini, R.: Content-based similarity retrieval of trademarks using relevance feedback. Pattern Recogn. 34(8), 1639\u20131655 (2001)","journal-title":"Pattern Recogn."},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Eakins, J.P., Edwards, J.D., Riley, K.J., Rosin, P.L.: Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images. In: Storage and Retrieval for Media Databases 2001, vol.\u00a04315, pp. 196\u2013207. SPIE (2001)","DOI":"10.1117\/12.410929"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Feng, Y., Shi, C., Qi, C., Xu, J., Xiao, B., Wang, C.: Aggregation of reversal invariant features from edge images for large-scale trademark retrieval. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR), pp. 384\u2013388. IEEE (2018)","DOI":"10.1109\/ICCAR.2018.8384705"},{"key":"19_CR14","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21271\u201321284 (2020)"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., Tao, D.: A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"issue":"6","key":"19_CR19","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1109\/83.923290","volume":"10","author":"S Hsieh","year":"2001","unstructured":"Hsieh, S., Fan, K.C.: Multiple classifiers for color flag and trademark image retrieval. IEEE Trans. Image Process. 10(6), 938\u2013950 (2001)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Hu, F., Chen, A., Wang, Z., Zhou, F., Dong, J., Li, X.: Lightweight attentional feature fusion: a new baseline for text-to-video retrieval. In: European Conference on Computer Vision, pp. 444\u2013461. Springer (2022)","DOI":"10.1007\/978-3-031-19781-9_26"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Ioannou, Y., Robertson, D., Cipolla, R., Criminisi, A.: Deep roots: improving CNN efficiency with hierarchical filter groups. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1231\u20131240 (2017)","DOI":"10.1109\/CVPR.2017.633"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Jimenez, A., Alvarez, J.M., Giro-i Nieto, X.: Class-weighted convolutional features for visual instance search. arXiv preprint arXiv:1707.02581 (2017)","DOI":"10.5244\/C.31.144"},{"key":"19_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-319-46604-0_48","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"Y Kalantidis","year":"2016","unstructured":"Kalantidis, Y., Mellina, C., Osindero, S.: Cross-dimensional weighting for aggregated deep convolutional features. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9913, pp. 685\u2013701. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46604-0_48"},{"key":"19_CR24","unstructured":"Kim, J., Yoon, S.E.: Regional attention based deep feature for image retrieval. In: BMVC, p. 209 (2018)"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Lan, T., Feng, X., Li, L., Xia, Z.: Similar trademark image retrieval based on convolutional neural network and constraint theory. In: 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp.\u00a01\u20136. IEEE (2018)","DOI":"10.1109\/IPTA.2018.8608162"},{"key":"19_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-319-71598-8_21","volume-title":"Image and Graphics","author":"T Lan","year":"2017","unstructured":"Lan, T., Feng, X., Xia, Z., Pan, S., Peng, J.: Similar trademark image retrieval integrating LBP and convolutional neural network. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10668, pp. 231\u2013242. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71598-8_21"},{"key":"19_CR27","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"19_CR28","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vision"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Luo, Y., Ren, X., Zheng, Z., Jiang, Z., Jiang, X., You, Y.: Came: confidence-guided adaptive memory efficient optimization. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4442\u20134453 (2023)","DOI":"10.18653\/v1\/2023.acl-long.243"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Perez, C.A., et al.: Trademark image retrieval using a combination of deep convolutional neural networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489045"},{"issue":"1","key":"19_CR31","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.cviu.2009.07.004","volume":"114","author":"R Phan","year":"2010","unstructured":"Phan, R., Androutsos, D.: Content-based retrieval of logo and trademarks in unconstrained color image databases using color edge gradient co-occurrence histograms. Comput. Vis. Image Underst. 114(1), 66\u201384 (2010)","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"19_CR32","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1007\/s11831-023-09884-2","volume":"30","author":"V Rani","year":"2023","unstructured":"Rani, V., Nabi, S.T., Kumar, M., Mittal, A., Kumar, K.: Self-supervised learning: a succinct review. Arch. Comput. Methods Eng. 30(4), 2761\u20132775 (2023)","journal-title":"Arch. Comput. Methods Eng."},{"key":"19_CR33","unstructured":"Robinson, J., Chuang, C.Y., Sra, S., Jegelka, S.: Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592 (2020)"},{"key":"19_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1007\/978-3-642-20161-5_32","volume-title":"Advances in Information Retrieval","author":"M Rusi\u00f1ol","year":"2011","unstructured":"Rusi\u00f1ol, M., Aldavert, D., Karatzas, D., Toledo, R., Llad\u00f3s, J.: Interactive trademark image retrieval by fusing semantic and visual content. In: Clough, P., et al. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 314\u2013325. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-20161-5_32"},{"key":"19_CR35","unstructured":"Sablayrolles, A., Douze, M., Schmid, C., J\u2019egou, H.: Spreading vectors for similarity search. arXiv preprint arXiv:1806.03198 (2018)"},{"key":"19_CR36","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., Hardmeier, H., Sattler, T., Pollefeys, M.: Comparative evaluation of hand-crafted and learned local features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1482\u20131491 (2017)","DOI":"10.1109\/CVPR.2017.736"},{"key":"19_CR37","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"19_CR38","unstructured":"Tolias, G., Sicre, R., J\u00e9gou, H.: Particular object retrieval with integral max-pooling of CNN activations. arXiv preprint arXiv:1511.05879 (2015)"},{"key":"19_CR39","doi-asserted-by":"crossref","unstructured":"Tursun, O., Sinan, K.: A challenging big dataset for benchmarking trademark retrieval. In: IAPR Conference on Machine Vision and Applications, p.\u00a028 (2015)","DOI":"10.1109\/MVA.2015.7153243"},{"key":"19_CR40","unstructured":"Tursun, O., Aker, C., Kalkan, S.: A large-scale dataset and benchmark for similar trademark retrieval. arXiv preprint arXiv:1701.05766 (2017)"},{"key":"19_CR41","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1109\/TIFS.2019.2959921","volume":"17","author":"O Tursun","year":"2019","unstructured":"Tursun, O., Denman, S., Sivapalan, S., Sridharan, S., Fookes, C., Mau, S.: Component-based attention for large-scale trademark retrieval. IEEE Trans. Inf. Forensics Secur. 17, 2350\u20132363 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"19_CR42","doi-asserted-by":"crossref","unstructured":"Tursun, O., Denman, S., Sridharan, S., Fookes, C.: Learning regional attention over multi-resolution deep convolutional features for trademark retrieval. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2393\u20132397. IEEE (2021)","DOI":"10.1109\/ICIP42928.2021.9506223"},{"key":"19_CR43","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"19_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"19_CR45","unstructured":"Wei, C., Wang, H., Shen, W., Yuille, A.: Co2: consistent contrast for unsupervised visual representation learning. arXiv preprint arXiv:2010.02217 (2020)"},{"key":"19_CR46","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"19_CR47","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"19_CR48","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: International Conference on Machine Learning, pp. 12310\u201312320. PMLR (2021)"},{"key":"19_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, T., Qi, G.J., Xiao, B., Wang, J.: Interleaved group convolutions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4373\u20134382 (2017)","DOI":"10.1109\/ICCV.2017.469"},{"issue":"5","key":"19_CR50","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1109\/TPAMI.2017.2709749","volume":"40","author":"L Zheng","year":"2017","unstructured":"Zheng, L., Yang, Y., Tian, Q.: Sift meets CNN: a decade survey of instance retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 40(5), 1224\u20131244 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR51","doi-asserted-by":"crossref","unstructured":"Zhu, W., Liu, J., Huang, Y.: HNSSL: hard negative-based self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4778\u20134787 (2023)","DOI":"10.1109\/CVPRW59228.2023.00506"}],"container-title":["Lecture Notes in Computer Science","Computational Visual Media"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-5815-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:43:38Z","timestamp":1745606618000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-5815-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819658145","9789819658152"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-5815-2_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Visual Media","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong SAR","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccvm.org\/2025\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}