  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 and leads to a 4.5-point gain in
detection mAP. Our implementation achieves this performance while running at
260 ms per image.
