  In a weakly-supervised scenario object detectors need to be trained using
image-level annotation alone. Since bounding-box-level ground truth is not
available, most of the solutions proposed so far are based on an iterative,
Multiple Instance Learning framework in which the current classifier is used to
select the highest-confidence boxes in each image, which are treated as
pseudo-ground truth in the next training iteration. However, the errors of an
immature classifier can make the process drift, usually introducing many of
false positives in the training dataset. To alleviate this problem, we propose
in this paper a training protocol based on the self-paced learning paradigm.
The main idea is to iteratively select a subset of images and boxes that are
the most reliable, and use them for training. While in the past few years
similar strategies have been adopted for SVMs and other classifiers, we are the
first showing that a self-paced approach can be used with deep-network-based
classifiers in an end-to-end training pipeline. The method we propose is built
on the fully-supervised Fast-RCNN architecture and can be applied to similar
architectures which represent the input image as a bag of boxes. We show
state-of-the-art results on Pascal VOC 2007, Pascal VOC 2010 and ILSVRC 2013.
On ILSVRC 2013 our results based on a low-capacity AlexNet network outperform
even those weakly-supervised approaches which are based on much higher-capacity
networks.
