[cosc-grad-students-list] FW: Mayank Agarwala MS Thesis Defense - 4/15 1:00pm "FSDCNN: A Few Shot Detection Mechanism that preserves its supervised nature"

[King, Scott]

From: King, Scott
Sent: Wednesday, April 14, 2021 12:08 PM
To: 'csci at listserv.tamucc.edu' <csci at listserv.tamucc.edu>; cosc-grad-students-list at listserv.tamucc.edu; scitech-list at listserv.tamucc.edu; gscs-student at listserv.tamucc.edu
Subject: Mayank Agarwala MS Thesis Defense - 4/15 1:00pm "FSDCNN: A Few Shot Detection Mechanism that preserves its supervised nature"

Please join us for a MS Thesis defense on Friday at 1:00PM (virtual, link below)

Mayank Agarwala
"FSDCNN: A Few Shot Detection Mechanism that preserves its supervised nature"

Abstract:
Object detection has become better with the advent of deep convolution neural 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.





Friday, April 16, 2021
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Scott King
Professor of Computer Science
Director, iCORE
Texas A&M University - Corpus Christi

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