King, Scott A.Agarwala, Mayank2021-10-062021-10-062021-05https://hdl.handle.net/1969.6/89768Object detection has become better with the advent of deep convolution neutral networks. However, the challenge of training a fully supervised system when there is a small amount of training samples available still remains. Another issue with fully supervised systems is seen upon encountering novel classes. It is difficult to retrain the model as it is a time-consuming and tedious process. Inspired by a human’s ability to learn at a rapid rate, few-shot learning models have seen rapid development. In contrast to fully supervised systems, these learn from just a few samples. We propose a few-shot detection model, FSDCNN, based on a two-stage detector, that optimizes both the region proposal network and the object detector with the help of few-shot learning. FSDCNN performs similar to other models when only 1 or 3 new samples are seen but outperforms them when 5 or 10 samples of the new classes are seen, and it preserves the fully supervised nature of the base two stage detector.78 pagesen-USThis material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher.deep learningfew-shotmeta learningneural networksText