leveraging both hand-crafted and CNN features for an optimal trade-off between accuracy and speed. The RCNN developed by Girshick et al. [@GirEtAl:cvpr14] nicely integrates an object proposal mechanism within CNN framework. This leads to SPPnet [@HeEtAl:eccv14] that improves the detection speed of RCNN by evaluating CNN features once per image. Built on top of RCNN and SPPnet, Fast-RCNN [@Gir:iccv15] brings up the idea of single-stage training and multi-task learning of both a classifier and a bounding box regressor. Furthermore, Faster-RCNN [@RenEtAl:nips15] considers a region proposal network that shares full-image convolutional features with the detection network to efficiently predict object proposals, instead of the time-consuming techniques such as selective search, leading to a significant speedup for proposal generation.

Multi-layer approaches have also been developed for detecting objects across multiple scales. [@CaiEtAl:eccv16; @HeEtAl:eccv14] attempt to alleviate the inconsistency of RPN [@RenEtAl:nips15] by employing an upsampling operation on inputs at both training and testing stages, which nevertheless is significantly more demanding in terms of computation and memory. SA-FastRCNN [@LiEtAl:cvpr15] develops a divide-and-conquer strategy based on Fast-RCNN that uses multiple built-in subnetworks to adaptively detect pedestrians across scales. Similarly, MS-CNN [@CaiEtAl:eccv16] works with multiple layers to match objects of different scales. SSD [@LiuEtAl:eccv16] discretizes 