02199nas a2200385   4500000000100000008004100001260005900042653002200101653002200123653001800145653001800163653002300181653002200204653002200226653001700248653003400265653002400299653002100323653002300344653002200367653002100389653002200410653001800432653001800450653001300468100001200481700001100493700001000504700001200514245007900526856015500605300001400760520101400774020002501788                                       d  bInstitute of Electrical and Electronics Engineers Inc.10aContrast Learning10aContrast learning10aDeep learning10aDeep learning10aDigital protection10aFew-Shot Learning10aFew-shot learning10aImage coding10aIntangible cultural heritages10aModern technologies10aObject detection10aObject recognition10aObjects detection10atarget detection10aTargets detection10aThangka Image10aThangka image10aTibetans1 aH. Tang1 aC. Yue1 aW. Hu1 aL. Qiao00aObject Detection of Few-Shot Thangka Images by Contrastive Proposal Coding  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85131818997&doi=10.1109%2fICSP54964.2022.9778717&partnerID=40&md5=0fa29c1adac1759b4a3ccb0333847bd5  a1916-19193 aAs one of China s intangible cultural heritage, Thangka is the main carrier of Tibetan culture. The application of modern technology in the digital protection of thangka images is of great significance to promote cultural exchange and inheritance. In this paper, the headdress, seat and handheld objects of the thangka images are taken as the detection targets, and they are classified and recognized by using the method,which is few-shot object detection by contrastive proposal coding. This method introduces contrastive learning into the few-shot object detection method. That takes Faster R-CNN as the baseline model, adds a contrastive branch parallel to the classification and prediction branch at the end of the model. In the contrastive branch, the problem of misclassification is alleviated by calculating the similarity score between the object proposal boxes. The experiment shows that this method can achieve good results on the thangka data set, with mAP reaching 38.1\% and AP50 reaching 51.3\%.  a9781665478571 (ISBN)