Unsupervised learning of foreground object detection

TL;DR

This paper introduces an unsupervised learning framework for foreground object detection that trains a student network to mimic a teacher's unsupervised discovery process, enabling fast and effective detection in images and videos.

Contribution

It presents a novel student-teacher training approach for unsupervised object detection that improves generalization and speed compared to previous methods.

Findings

Achieves top results on three datasets for object discovery, segmentation, and saliency detection.

Runs one to two orders of magnitude faster than existing unsupervised methods.

Supports multiple generations of training for improved performance.

Abstract

Unsupervised learning poses one of the most difficult challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large quantities of unlabeled videos can be collected at relatively low cost. In this paper, we address the unsupervised learning problem in the context of detecting the main foreground objects in single images. We train a student deep network to predict the output of a teacher pathway that performs unsupervised object discovery in videos or large image collections. Our approach is different from published methods on unsupervised object discovery. We move the unsupervised learning phase during training time, then at test time we apply the standard feed-forward processing along the student pathway. This strategy has the benefit of allowing increased generalization possibilities during training, while remaining fast at testing. Our unsupervised learning algorithm can run over several generations of student-teacher training. Thus, a group of student networks trained in the first generation collectively create the teacher at the next generation. In experiments our method achieves top results on three current datasets for object discovery in video, unsupervised image segmentation and saliency detection. At test time the proposed system is fast, being one to two orders of magnitude faster than published unsupervised methods.