{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:23:21Z","timestamp":1760235801194,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"R&amp;D projects in key areas of Guangdong Province of China","award":["2018B010109007","2019B010153002"],"award-info":[{"award-number":["2018B010109007","2019B010153002"]}]},{"name":"Guangzhou R&amp;D Programme in Key Areas of Science and Technology Projects","award":["202007040006"],"award-info":[{"award-number":["202007040006"]}]},{"name":"Guangdong Jiont Funds of the National Natural Science Foundation of China","award":["U1801263"],"award-info":[{"award-number":["U1801263"]}]},{"name":"National High-Resolution Earth Observation Major Project","award":["83-Y40G33-9001-18\/20"],"award-info":[{"award-number":["83-Y40G33-9001-18\/20"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The datasets in the latest semantic segmentation model often need to be manually labeled for each pixel, which is time-consuming and requires much effort. General models are unable to make better predictions, for new categories of information that have never been seen before, than the few-shot segmentation that has emerged. However, the few-shot segmentation is still faced up with two challenges. One is the inadequate exploration of semantic information conveyed in the high-level features, and the other is the inconsistency of segmenting objects at different scales. To solve these two problems, we have proposed a prior feature matching network (PFMNet). It includes two novel modules: (1) the Query Feature Enhancement Module (QFEM), which makes full use of the high-level semantic information in the support set to enhance the query feature, and (2) the multi-scale feature matching module (MSFMM), which increases the matching probability of multi-scales of objects. Our method achieves an intersection over union average score of 61.3% for one-shot segmentation and 63.4% for five-shot segmentation, which surpasses the state-of-the-art results by 0.5% and 1.5%, respectively.<\/jats:p>","DOI":"10.3390\/info12100406","type":"journal-article","created":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T10:55:40Z","timestamp":1633085740000},"page":"406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PFMNet: Few-Shot Segmentation with Query Feature Enhancement and Multi-Scale Feature Matching"],"prefix":"10.3390","volume":"12","author":[{"given":"Jingyao","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Lianglun","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Zewen","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Jiahong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University of Technology, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3360-1756","authenticated-orcid":false,"given":"Genping","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Zeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Dark Matter AI, Xiangjiang International Sci & Tech Center, Guangzhou 510060, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cai, Q., Pan, Y., Yao, T., Yan, C., and Mei, T. 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