1 research outputs found
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Being inspired by child's learning experience - taught first and followed by
observation and questioning, we investigate a critically supervised learning
methodology for object detection in this work. Specifically, we propose a
taught-observe-ask (TOA) method that consists of several novel components such
as negative object proposal, critical example mining, and machine-guided
question-answer (QA) labeling. To consider labeling time and performance
jointly, new evaluation methods are developed to compare the performance of the
TOA method, with the fully and weakly supervised learning methods. Extensive
experiments are conducted on the PASCAL VOC and the Caltech benchmark datasets.
The TOA method provides significantly improved performance of weakly
supervision yet demands only about 3-6% of labeling time of full supervision.
The effectiveness of each novel component is also analyzed