Title: Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm,
Abstract: Object detection when provided image-level labels instead of instance-level
labels (i.e., bounding boxes) during training is an important problem in
computer vision, since large scale image datasets with instance-level labels
are extremely costly to obtain. In this paper, we address this challenging
problem by developing an Expectation-Maximization (EM) based object detection
method using deep convolutional neural networks (CNNs). Our method is
applicable to both the weakly-supervised and semi-supervised settings.
Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly
supervised setting, our method provides significant detection performance
improvement over current state-of-the-art methods, (2) having access to a small
number of strongly (instance-level) annotated images, our method can almost
match the performace of the fully supervised Fast RCNN. We share our source
code at this https URL.