{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:21:26Z","timestamp":1770394886520,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557578","type":"print"},{"value":"9789819557585","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5758-5_20","type":"book-chapter","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:59:51Z","timestamp":1770353991000},"page":"266-280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Object Detection via Superclass-Guided Feature Enhancement"],"prefix":"10.1007","author":[{"given":"Huajie","family":"Xu","sequence":"first","affiliation":[]},{"given":"Haikun","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Yuanzhuo","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,7]]},"reference":[{"issue":"7","key":"20_CR1","doi-asserted-by":"publisher","first-page":"5963","DOI":"10.1109\/TCSVT.2023.3343397","volume":"34","author":"H Chen","year":"2024","unstructured":"Chen, H., Wang, Q., Xie, K., et al.: SD-FSOD: self-distillation paradigm via distribution calibration for few-shot object detection. IEEE Trans. Circuits Syst. Video Technol. 34(7), 5963\u20135976 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"8","key":"20_CR2","doi-asserted-by":"publisher","first-page":"7121","DOI":"10.1109\/TCSVT.2024.3370600","volume":"34","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Wang, Q., Dong, X., et al.: FSNA: few-shot object detection via neighborhood information adaption and all attention. IEEE Trans. Circuits Syst. Video Technol. 34(8), 7121\u20137134 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Fan, Q., Zhuo, W., Tang, C., et al.: Few-shot object detection with attention-RPN and multi-relation detector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4013\u20134022 (2020)","DOI":"10.1109\/CVPR42600.2020.00407"},{"issue":"3","key":"20_CR4","first-page":"3090","volume":"45","author":"Y Xiao","year":"2022","unstructured":"Xiao, Y., Lepetit, V., Marlet, R.: Few-shot object detection and viewpoint estimation for objects in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3090\u20133106 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"20_CR5","first-page":"12832","volume":"45","author":"G Zhang","year":"2023","unstructured":"Zhang, G., Luo, Z., Cui, K., et al.: Meta-DETR: Image-level few-shot detection with inter-class correlation exploitation. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 12832\u201312843 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR6","unstructured":"Wang, X., Huang, T., Darrell, T., et al.: Frustratingly simple few-shot object detection. In: 37th International Conference on Machine Learning: ICML, pp. 9919\u20139928 (2021)"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Ma, J., Niu, Y., Xu, J., et al.: DiGeo: Discriminative Geometry-aware learning for generalized few-shot object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3208\u20133218 (2023)","DOI":"10.1109\/CVPR52729.2023.00313"},{"issue":"4","key":"20_CR8","doi-asserted-by":"publisher","first-page":"5435","DOI":"10.1109\/TNNLS.2022.3204597","volume":"35","author":"D Qi","year":"2024","unstructured":"Qi, D., Hu, J., Shen, J.: Few-shot object detection with self-supervising and cooperative classifier. IEEE Trans. Neural Netw. Learn. Syst. 35(4), 5435\u20135446 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Yan, X., Chen, Z., Xu, A., et al.: Meta R-CNN: towards general solver for instance-level low-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9577\u20139586 (2019)","DOI":"10.1109\/ICCV.2019.00967"},{"issue":"4","key":"20_CR10","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s11760-024-03774-1","volume":"19","author":"W Liu","year":"2025","unstructured":"Liu, W., Cai, X., Wang, C., et al.: Dynamic relevance learning for few-shot object detection. Sig. Image Video Process 19(4), 297 (2025)","journal-title":"Sig. Image Video Process"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Han, G., He, Y., Huang, S., et al.: Query adaptive few-shot object detection with heterogeneous graph convolutional networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3263\u20133272 (2021)","DOI":"10.1109\/ICCV48922.2021.00325"},{"key":"20_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103349","volume":"216","author":"S Wu","year":"2022","unstructured":"Wu, S., Kankanhalli, M., Tung, A.: Superclass-aware network for few-shot learning. Comput. Vis. Image Underst. 216, 103349 (2022)","journal-title":"Comput. Vis. Image Underst."},{"issue":"12","key":"20_CR13","first-page":"1242","volume":"33","author":"K Wang","year":"2008","unstructured":"Wang, K., Zhang, J., Li, D., et al.: Adaptive affinity propagation clustering. Acta Autom. Sin. 33(12), 1242\u20131246 (2008)","journal-title":"Acta Autom. Sin."},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2021)","DOI":"10.1109\/CVPR46437.2021.00252"},{"issue":"2","key":"20_CR15","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van, G., Williams, C., et al.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Lin T., Maire M., Belongie S., et al.: Microsoft COCO: common objects in context. In: Proceedings of the European Conference on Computer Vision, pp. 740\u2013755 (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Sun, B., Li, B., Cai, S., et al.: FSCE: Few-shot object detection via contrastive proposal encoding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7352\u20137362 (2021)","DOI":"10.1109\/CVPR46437.2021.00727"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Shangguan, Z., Huai, L., Liu, T., et al.: Few-shot object detection with refined contrastive learning. In: IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 991\u2013996 (2023)","DOI":"10.1109\/ICTAI59109.2023.00148"},{"issue":"5","key":"20_CR19","doi-asserted-by":"publisher","first-page":"8072","DOI":"10.1109\/TNNLS.2024.3422216","volume":"36","author":"C Liu","year":"2025","unstructured":"Liu, C., Li, B., Shi, M., et al.: Explicit margin equilibrium for few-shot object detection. IEEE Trans. Neural Netw. Learn. Syst. 36(5), 8072\u20138084 (2025)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"20_CR20","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"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\/978-981-95-5758-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:59:53Z","timestamp":1770353993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5758-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557578","9789819557585"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5758-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Shanghai","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}