{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:26Z","timestamp":1729225706573,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Few-shot object detection aims to effectively detect novel classes with limited annotated samples. Due to the low-quality instance features obtained by deep learning in data-scarce scenarios, few-shot object detection remains a significant challenge. In this paper, a novel method is proposed to enhance the few-shot object detection performance by focusing on the generalizability and discriminability of instance features. In detail, by synthesizing auxiliary instances that embed diverse attributes, a mask-guided instance feature generation module is presented to alleviate the overfitting on sample-specific characteristics, thereby facilitating the acquisition of generalizable object-relevant knowledge. Then, to learn discriminative object-relevant attributes, a hybrid cross-layer and intra-layer contrastive learning mechanism is designed to enhance the discriminability of instance features by building contrastive constraints between instances within and across layers. Experimental results on two widely used benchmarks demonstrate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3233\/faia240558","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:49:53Z","timestamp":1729169393000},"source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Object Detection with Instance Feature Generation and Hybrid Contrastive Learning"],"prefix":"10.3233","author":[{"given":"Yuhui","family":"Wang","sequence":"first","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Peng","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyi","family":"Qin","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Song","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240558","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:49:54Z","timestamp":1729169394000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240558"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240558","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}