  Object proposal is essential for current state-of-the-art object detection
pipelines. However, the existing proposal methods generally fail in producing
results with satisfying localization accuracy. The case is even worse for small
objects which however are quite common in practice. In this paper we propose a
novel Scale-aware Pixel-wise Object Proposal (SPOP) network to tackle the
challenges. The SPOP network can generate proposals with high recall rate and
average best overlap (ABO), even for small objects. In particular, in order to
improve the localization accuracy, a fully convolutional network is employed
which predicts locations of object proposals for each pixel. The produced
ensemble of pixel-wise object proposals enhances the chance of hitting the
object significantly without incurring heavy extra computational cost. To solve
the challenge of localizing objects at small scale, two localization networks
which are specialized for localizing objects with different scales are
introduced, following the divide-and-conquer philosophy. Location outputs of
these two networks are then adaptively combined to generate the final proposals
by a large-/small-size weighting network. Extensive evaluations on PASCAL VOC
2007 show the SPOP network is superior over the state-of-the-art models. The
high-quality proposals from SPOP network also significantly improve the mean
average precision (mAP) of object detection with Fast-RCNN framework. Finally,
the SPOP network (trained on PASCAL VOC) shows great generalization performance
when testing it on ILSVRC 2013 validation set.
