{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:40:21Z","timestamp":1727592021580},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732348","type":"print"},{"value":"9783031732355","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73235-5_5","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:01:53Z","timestamp":1727589713000},"page":"75-91","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AFreeCA: Annotation-Free Counting for\u00a0All"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0009-0004-1791-8843","authenticated-orcid":false,"given":"Adriano","family":"D\u2019Alessandro","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4693-3565","authenticated-orcid":false,"given":"Ali","family":"Mahdavi-Amiri","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5040-7448","authenticated-orcid":false,"given":"Ghassan","family":"Hamarneh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Arteta, C., Lempitsky, V., Zisserman, A.: Counting in the wild. In: European Conference on Computer Vision (2016)","DOI":"10.1007\/978-3-319-46478-7_30"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-031-19821-2_11","volume-title":"Computer Vision - ECCV 2022","author":"D Babu Sam","year":"2022","unstructured":"Babu Sam, D., Agarwalla, A., Joseph, J., Sindagi, V.A., Babu, R.V., Patel, V.M.: Completely self-supervised crowd counting via distribution matching. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022, pp. 186\u2013204. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_11"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00465"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, Z.Q., Dai, Q., Li, H., Song, J., Wu, X., Hauptmann, A.G.: Rethinking spatial invariance of convolutional networks for object counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19638\u201319648 (2022)","DOI":"10.1109\/CVPR52688.2022.01902"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"D\u2019Alessandro, A.C., Mahdavi-Amiri, A., Hamarneh, G.: Learning-to-count by learning-to-rank. In: 2023 20th Conference on Robots and Vision (CRV), pp. 105\u2013112 (2023). https:\/\/doi.org\/10.1109\/CRV60082.2023.00021","DOI":"10.1109\/CRV60082.2023.00021"},{"key":"5_CR6","unstructured":"Gong, Y., Mori, G., Tung, F.: RankSim: ranking similarity regularization for deep imbalanced regression. In: International Conference on Machine Learning (ICML) (2022)"},{"key":"5_CR7","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":"5_CR8","unstructured":"He, R., et al.: Is synthetic data from generative models ready for image recognition? In: The Eleventh International Conference on Learning Representations (ICLR) (2023). https:\/\/openreview.net\/forum?id=nUmCcZ5RKF"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Hsieh, M.R., Lin, Y.L., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal networks. In: The IEEE International Conference on Computer Vision (ICCV). IEEE (2017)","DOI":"10.1109\/ICCV.2017.446"},{"key":"5_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/978-3-030-58542-6_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Hu","year":"2020","unstructured":"Hu, Y., Jiang, X., Liu, X., Zhang, B., Han, J., Cao, X., Doermann, D.: NAS-count: counting-by-density with neural architecture search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 747\u2013766. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58542-6_45"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Idrees, H., et al.: Composition loss for counting, density map estimation and localization in dense crowds. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 532\u2013546 (2018)","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, R., Liu, L., Chen, C.: Clip-count: towards text-guided zero-shot object counting. In: Proceedings of the 31st ACM International Conference on Multimedia, MM 2023, pp. 4535\u20134545. Association for Computing Machinery, New York (2023)","DOI":"10.1145\/3581783.3611789"},{"key":"5_CR13","unstructured":"Kumar, A., Raghunathan, A., Jones, R.M., Ma, T., Liang, P.: Fine-tuning can distort pretrained features and underperform out-of-distribution. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=UYneFzXSJWh"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Lee, K.H., He, X., Zhang, L., Yang, L.: Cleannet: transfer learning for scalable image classifier training with label noise. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00571"},{"key":"5_CR15","unstructured":"Lempitsky, V., Zisserman, A.: Learning to count objects in images. Adv. Neural Inf. Process. Syst. 23 (2010)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., Chen, D.: CSRNET: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Liang, D., Xie, J., Zou, Z., Ye, X., Xu, W., Bai, X.: Crowdclip: unsupervised crowd counting via vision-language model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2893\u20132903 (2023)","DOI":"10.1109\/CVPR52729.2023.00283"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Liu, X., Van De\u00a0Weijer, J., Bagdanov, A.D.: Leveraging unlabeled data for crowd counting by learning to rank. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7661\u20137669 (2018)","DOI":"10.1109\/CVPR.2018.00799"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Paiss, R., et al.: Teaching clip to count to ten. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3170\u20133180 (2023)","DOI":"10.1109\/ICCV51070.2023.00294"},{"key":"5_CR20","unstructured":"Pogan\u010di\u0107, M.V., Paulus, A., Musil, V., Martius, G., Rolinek, M.: Differentiation of blackbox combinatorial solvers. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=BkevoJSYPB"},{"key":"5_CR21","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Ranjan, V., Sharma, U., Nguyen, T., Hoai, M.: Learning to count everything. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00340"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Shi, M., Hao, L., Feng, C., Liu, C., Cao, Z.: Represent, compare, and learn: a similarity-aware framework for class-agnostic counting. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00931"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Shipard, J., Wiliem, A., Thanh, K.N., Xiang, W., Fookes, C.: Diversity is definitely needed: improving model-agnostic zero-shot classification via stable diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 769\u2013778 (2023)","DOI":"10.1109\/CVPRW59228.2023.00084"},{"issue":"5","key":"5_CR26","first-page":"2594","volume":"44","author":"VA Sindagi","year":"2020","unstructured":"Sindagi, V.A., Yasarla, R., Patel, V.M.: Jhu-crowd++: large-scale crowd counting dataset and a benchmark method. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2594\u20132609 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR27","unstructured":"Trivedi, P., Koutra, D., Thiagarajan, J.J.: A closer look at model adaptation using feature distortion and simplicity bias. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=wkg_b4-IwTZ"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Wan, J., Liu, Z., Chan, A.B.: A generalized loss function for crowd counting and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1974\u20131983 (2021)","DOI":"10.1109\/CVPR46437.2021.00201"},{"key":"5_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3013269","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Gao, J., Lin, W., Li, X.: Nwpu-crowd: a large-scale benchmark for crowd counting and localization. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.3013269","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR30","unstructured":"Wu, P., Zheng, S., Goswami, M., Metaxas, D., Chen, C.: A topological filter for learning with label noise. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 21382\u201321393. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/f4e3ce3e7b581ff32e40968298ba013d-Paper.pdf"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"},{"key":"5_CR32","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1452\u20131464 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR33","unstructured":"Zhu, Z., Dong, Z., Liu, Y.: Detecting corrupted labels without training a model to predict. In: Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0162, pp. 27412\u201327427. PMLR (2022). https:\/\/proceedings.mlr.press\/v162\/zhu22a.html"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73235-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T06:11:01Z","timestamp":1727590261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73235-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031732348","9783031732355"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73235-5_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}