  It is well known that attention mechanisms can effectively improve the
performance of many CNNs including object detectors. Instead of refining
feature maps prevalently, we reduce the prohibitive computational complexity by
a novel attempt at attention. Therefore, we introduce an efficient object
detector called Selective Convolutional Network (SCN), which selectively
calculates only on the locations that contain meaningful and conducive
information. The basic idea is to exclude the insignificant background areas,
which effectively reduces the computational cost especially during the feature
extraction. To solve it, we design an elaborate structure with negligible
overheads to guide the network where to look next. It's end-to-end trainable
and easy-embedding. Without additional segmentation datasets, we explores two
different train strategies including direct supervision and indirect
supervision. Extensive experiments assess the performance on PASCAL VOC2007 and
MS COCO detection datasets. Results show that SSD and Pelee integrated with our
method averagely reduce the calculations in a range of 1/5 and 1/3 with slight
loss of accuracy, demonstrating the feasibility of SCN.
