@inproceedings{11466,
  keywords = {Contrast Learning, Contrast learning, Deep learning, Deep learning, Digital protection, Few-Shot Learning, Few-shot learning, Image coding, Intangible cultural heritages, Modern technologies, Object detection, Object recognition, Objects detection, target detection, Targets detection, Thangka Image, Thangka image, Tibetans},
  author = {H. Tang and C. Yue and W. Hu and L. Qiao},
  title = {Object Detection of Few-Shot Thangka Images by Contrastive Proposal Coding},
  abstract = {As 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\%.},
  booktitle = {Int. Conf. Intell. Comput. Signal Process., ICSP},
  pages = {1916-1919},
  publisher = {Institute of Electrical and Electronics Engineers Inc.},
  school = {Institute of Electrical and Electronics Engineers Inc.},
  isbn = {9781665478571 (ISBN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131818997&doi=10.1109%2fICSP54964.2022.9778717&partnerID=40&md5=0fa29c1adac1759b4a3ccb0333847bd5},
  doi = {10.1109/ICSP54964.2022.9778717},
  note = {Journal Abbreviation: Int. Conf. Intell. Comput. Signal Process., ICSP},
}