the output space of bounding boxes into a set of template boxes over varying aspect ratios and scales. Complementary detectors are then utilized at different output layers that collectively give rise to a strong multi-scale detector. A strategy adopted by some of these methods [@GirEtAl:cvpr14; @DolEtAl:bmvc10] is to train a single classifier at a fixed resolution, meanwhile the input image is rescaled to several distinct sizes, and the associated features are re-computed as independent inputs. By aiming to attain such scale-invariancy property, empirically this strategy produces improved detection results, at a price of being computationally very costly. Another strategy adopted by [@BenEtAl:cvpr12; @FelEtAl:tpami10; @SerEtAl:cvpr13; @EnzGav:tip11] is to simultaneously engage multiple object detectors to a single resolution of the input image, with each focusing on a unique local patch size. This strategy avoids the repeated computation of feature maps and tends to be more efficient, nonetheless, it is often difficult to sufficiently represent these feature responses within a single resolution. More recent works such as Faster-RCNN [@RenEtAl:nips15] address the issue with a multi-layer region proposal network (RPN), which achieves excellent object detection performance. However, RPN generates multi-layer proposals by sliding a fixed set of filters over a fixed set of convolutional 