DeepBox: Learning Objectness with Convolutional Networks

Weicheng Kuo, Bharath Hariharan, Jitendra Malik

Published 2015-05-08Version 1

Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before. Finally, DeepBox has a significant impact on the performance of the object detector, reducing by 40% the total time taken for detection.