  In this paper, we propose a zoom-out-and-in network for generating object
proposals. A key observation is that it is difficult to classify anchors of
different sizes with the same set of features. Anchors of different sizes
should be placed accordingly based on different depth within a network: smaller
boxes on high-resolution layers with a smaller stride while larger boxes on
low-resolution counterparts with a larger stride. Inspired by the conv/deconv
structure, we fully leverage the low-level local details and high-level
regional semantics from two feature map streams, which are complimentary to
each other, to identify the objectness in an image. A map attention decision
(MAD) unit is further proposed to aggressively search for neuron activations
among two streams and attend the most contributive ones on the feature learning
of the final loss. The unit serves as a decisionmaker to adaptively activate
maps along certain channels with the solely purpose of optimizing the overall
training loss. One advantage of MAD is that the learned weights enforced on
each feature channel is predicted on-the-fly based on the input context, which
is more suitable than the fixed enforcement of a convolutional kernel.
Experimental results on three datasets, including PASCAL VOC 2007, ImageNet
DET, MS COCO, demonstrate the effectiveness of our proposed algorithm over
other state-of-the-arts, in terms of average recall (AR) for region proposal
and average precision (AP) for object detection.
