{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T03:13:21Z","timestamp":1778901201611,"version":"3.51.4"},"reference-count":91,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62036012"],"award-info":[{"award-number":["62036012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072455"],"award-info":[{"award-number":["62072455"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61721004"],"award-info":[{"award-number":["61721004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L201001"],"award-info":[{"award-number":["L201001"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tip.2023.3239197","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T18:38:38Z","timestamp":1674844718000},"page":"1092-1107","source":"Crossref","is-referenced-by-count":28,"title":["Category Knowledge-Guided Parameter Calibration for Few-Shot Object Detection"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7970-7698","authenticated-orcid":false,"given":"Chaofan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoshan","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6499-0128","authenticated-orcid":false,"given":"Jinpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intelligent Science and Technology Academy, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Dong","sequence":"additional","affiliation":[{"name":"Intelligent Science and Technology Academy, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8343-9665","authenticated-orcid":false,"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","first-page":"9919","article-title":"Frustratingly simple few-shot object detection","volume":"119","author":"wang","year":"2020","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref57","first-page":"6353","article-title":"Generalized and discriminative few-shot object detection via SVD-dictionary enhancement","author":"wu","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00851"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01861"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00727"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01005"},{"key":"ref14","first-page":"456","article-title":"Multi-scale positive sample refinement for few-shot object detection","volume":"12361","author":"wu","year":"2020","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00407"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2965534"},{"key":"ref52","first-page":"3521","article-title":"Restoring negative information in few-shot object detection","author":"yang","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00967"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3025814"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01002"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58520-4_12"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00325"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00867"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6957"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SMC42975.2020.9283497"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11716"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3072811"},{"key":"ref91","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/s10803-009-0816-2"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref45","first-page":"1","article-title":"Graph attention networks","author":"velickovic","year":"2018","journal-title":"Proc 6th Int Conf Learn Represent (ICLR)"},{"key":"ref89","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01204-1"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-013-9406-y"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00525"},{"key":"ref41","first-page":"1","article-title":"Meta-learning with latent embedding optimization","author":"rusu","year":"2019","journal-title":"Proc 7th Int Conf Learn Represent (ICLR)"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01384"},{"key":"ref44","first-page":"1025","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref88","article-title":"Pvtv2: Improved baselines with pyramid vision transformer","author":"wang","year":"2021","journal-title":"arXiv 2106 13797"},{"key":"ref43","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc 5th Int Conf Learn Represent (ICLR)"},{"key":"ref87","first-page":"1","article-title":"Deformable DETR: Deformable transformers for end-to-end object detection","author":"zhu","year":"2021","journal-title":"Proc 9th Int Conf Learn Represent (ICLR)"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3093380"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2844175"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref3","first-page":"6517","article-title":"YOLO9000: Better, faster, stronger","author":"redmon","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00311"},{"key":"ref81","article-title":"Universal-prototype augmentation for few-shot object detection","author":"wu","year":"2021","journal-title":"arXiv 2103 01077v2"},{"key":"ref40","first-page":"1","article-title":"Learning to learn with conditional class dependencies","author":"jiang","year":"2019","journal-title":"Proc 7th Int Conf Learn Represent (ICLR)"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195735"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19959"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01514"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3142530"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206532"},{"key":"ref34","first-page":"1","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","author":"liu","year":"2019","journal-title":"Proc 7th Int Conf Learn Represent (ICLR)"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.119"},{"key":"ref37","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref36","first-page":"1","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"2017","journal-title":"Proc 5th Int Conf Learn Represent (ICLR)"},{"key":"ref31","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","author":"koch","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn (ICML) Workshop"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00507"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.776"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2007.09.009"},{"key":"ref32","first-page":"1","article-title":"Few-shot learning with graph neural networks","author":"satorras","year":"2018","journal-title":"Proc 6th Int Conf Learn Represent (ICLR)"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","first-page":"21","article-title":"SSD: Single shot MultiBox detector","volume":"9905","author":"liu","year":"2016","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref39","article-title":"On first-order meta-learning algorithms","author":"nichol","year":"2018","journal-title":"arXiv 1803 02999"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3143692"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3086590"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00145"},{"key":"ref73","first-page":"742","article-title":"Learning efficient object detection models with knowledge distillation","author":"chen","year":"2017","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3056895"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref68","first-page":"1","article-title":"Do deep convolutional nets really need to be deep and convolutional?","author":"urban","year":"2017","journal-title":"Proc 5th Int Conf Learn Represent (ICLR)"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref67","first-page":"3438","article-title":"Bayesian dark knowledge","author":"korattikara","year":"2015","journal-title":"Proc Adv Neural Inf Syst (NIPS)"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref25","first-page":"740","article-title":"Microsoft COCO: Common objects in context","volume":"8693","author":"lin","year":"2014","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref69","first-page":"1","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"zagoruyko","year":"2017","journal-title":"Proc 5th Int Conf Learn Represent (ICLR)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00387"},{"key":"ref64","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"arXiv 1606 09375"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2879624"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01281"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10449"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00534"},{"key":"ref65","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"ArXiv 1503 02531"},{"key":"ref28","first-page":"3630","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"Proc NIPS"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.328"},{"key":"ref29","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00728"},{"key":"ref62","first-page":"1","article-title":"Spectral networks and locally connected networks on graphs","author":"bruna","year":"2014","journal-title":"Proc 2nd Int Conf Learn Represent (ICLR)"},{"key":"ref61","first-page":"2721","article-title":"One-shot object detection with co-attention and co-excitation","author":"hsieh","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9991910\/10028751.pdf?arnumber=10028751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T19:07:53Z","timestamp":1677524873000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10028751\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":91,"URL":"https:\/\/doi.org\/10.1109\/tip.2023.3239197","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}