{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:42:19Z","timestamp":1783100539132,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819984619","type":"print"},{"value":"9789819984626","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8462-6_38","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"465-477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Transformer-Based Adaptive Semantic Aggregation Method for\u00a0UAV Visual Geo-Localization"],"prefix":"10.1007","author":[{"given":"Shishen","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuiwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaijun","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaokui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"issue":"15","key":"38_CR1","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.3390\/rs12152445","volume":"12","author":"W Chivasa","year":"2020","unstructured":"Chivasa, W., Mutanga, O., Biradar, C.: Uav-based multispectral phenotyping for disease resistance to accelerate crop improvement under changing climate conditions. Remote Sensing 12(15), 2445 (2020)","journal-title":"Remote Sensing"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Rizk, M., Slim, F., Charara, J.: Toward ai-assisted uav for human detection in search and rescue missions. In: DASA, pp. 781\u2013786. IEEE (2021)","DOI":"10.1109\/DASA53625.2021.9682412"},{"issue":"13","key":"38_CR3","doi-asserted-by":"publisher","first-page":"3205","DOI":"10.3390\/rs14133205","volume":"14","author":"S Ecke","year":"2022","unstructured":"Ecke, S., Dempewolf, J., Frey, J., Schwaller, A., Endres, E., Klemmt, H.J., Tiede, D., Seifert, T.: Uav-based forest health monitoring: a systematic review. Remote Sensing 14(13), 3205\u20133249 (2022)","journal-title":"Remote Sensing"},{"issue":"8","key":"38_CR4","first-page":"3158","volume":"22","author":"LC Chiu","year":"2013","unstructured":"Chiu, L.C., Chang, T.S., Chen, J.Y., Chang, N.Y.C.: Fast sift design for real-time visual feature extraction. TIP 22(8), 3158\u20133167 (2013)","journal-title":"TIP"},{"issue":"2","key":"38_CR5","first-page":"867","volume":"32","author":"T Wang","year":"2021","unstructured":"Wang, T., Zheng, Z., Yan, C., Zhang, J., Sun, Y., Zheng, B., Yang, Y.: Each part matters: local patterns facilitate cross-view geo-localization. TCSVT 32(2), 867\u2013879 (2021)","journal-title":"TCSVT"},{"issue":"7","key":"38_CR6","first-page":"4376","volume":"32","author":"M Dai","year":"2022","unstructured":"Dai, M., Hu, J., Zhuang, J., Zheng, E.: A transformer-based feature segmentation and region alignment method for uav-view geo-localization. TCSVT 32(7), 4376\u20134389 (2022)","journal-title":"TCSVT"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wei, Y., Yang, Y.: University-1652: a multi-view multi-source benchmark for drone-based geo-localization. In: ACM MM, pp. 1395\u20131403 (2020)","DOI":"10.1145\/3394171.3413896"},{"issue":"1","key":"38_CR8","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/rs13010047","volume":"13","author":"L Ding","year":"2020","unstructured":"Ding, L., Zhou, J., Meng, L., Long, Z.: A practical cross-view image matching method between uav and satellite for uav-based geo-localization. Remote Sensing 13(1), 47 (2020)","journal-title":"Remote Sensing"},{"issue":"7","key":"38_CR9","first-page":"4804","volume":"32","author":"X Tian","year":"2021","unstructured":"Tian, X., Shao, J., Ouyang, D., Shen, H.T.: Uav-satellite view synthesis for cross-view geo-localization. TCSVT 32(7), 4804\u20134815 (2021)","journal-title":"TCSVT"},{"issue":"19","key":"38_CR10","doi-asserted-by":"publisher","first-page":"3979","DOI":"10.3390\/rs13193979","volume":"13","author":"J Zhuang","year":"2021","unstructured":"Zhuang, J., Dai, M., Chen, X., Zheng, E.: A faster and more effective cross-view matching method of uav and satellite images for uav geolocalization. Remote Sensing 13(19), 3979 (2021)","journal-title":"Remote Sensing"},{"key":"38_CR11","first-page":"3780","volume":"31","author":"J Lin","year":"2022","unstructured":"Lin, J., Zheng, Z., Zhong, Z., Luo, Z., Li, S., Yang, Y., Sebe, N.: Joint representation learning and keypoint detection for cross-view geo-localization. TIP 31, 3780\u20133792 (2022)","journal-title":"TIP"},{"key":"38_CR12","doi-asserted-by":"publisher","first-page":"34277","DOI":"10.1109\/ACCESS.2022.3162693","volume":"10","author":"J Zhuang","year":"2022","unstructured":"Zhuang, J., Chen, X., Dai, M., Lan, W., Cai, Y., Zheng, E.: A semantic guidance and transformer-based matching method for uavs and satellite images for uav geo-localization. IEEE Access 10, 34277\u201334287 (2022)","journal-title":"IEEE Access"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., Shah, R.: Signature verification using a \u201csiamese\u201d time delay neural network. Neurips 6 (1993)","DOI":"10.1142\/9789812797926_0003"},{"key":"38_CR14","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R., et al.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2. Lille (2015)"},{"key":"38_CR15","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"38_CR17","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"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8462-6_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:07:19Z","timestamp":1703531239000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8462-6_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819984619","9789819984626"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8462-6_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}