  Current high-quality object detection approaches use the scheme of
salience-based object proposal methods followed by post-classification using
deep convolutional features. This spurred recent research in improving object
proposal methods. However, domain agnostic proposal generation has the
principal drawback that the proposals come unranked or with very weak ranking,
making it hard to trade-off quality for running time. This raises the more
fundamental question of whether high-quality proposal generation requires
careful engineering or can be derived just from data alone. We demonstrate that
learning-based proposal methods can effectively match the performance of
hand-engineered methods while allowing for very efficient runtime-quality
trade-offs. Using the multi-scale convolutional MultiBox (MSC-MultiBox)
approach, we substantially advance the state-of-the-art on the ILSVRC 2014
detection challenge data set, with $0.5$ mAP for a single model and $0.52$ mAP
for an ensemble of two models. MSC-Multibox significantly improves the proposal
quality over its predecessor MultiBox~method: AP increases from $0.42$ to
$0.53$ for the ILSVRC detection challenge. Finally, we demonstrate improved
bounding-box recall compared to Multiscale Combinatorial Grouping with less
proposals on the Microsoft-COCO data set.
