critical question arises: *is it possible to develop an elegant framework which can smartly associate the best of both methodologies and eliminate their major demerits?* We answer this question by trying to bridge the gap between the region-based and region-free methodologies. To achieve this goal, we focus on two fundamental problems: (a) Multi-scale object localization. Objects of various scales could appear at any position of an image, so tens of thousands of regions with different positions/scales/aspect ratios should be considered. Prior works [@hypernet][@ion] show that multi-scale representation will significantly improve object detection of various scales. However, these methods always detect *various scales of objects* at *one layer* of a network [@hypernet][@fasterrcnn][@rfcn]. With the proposed reverse connection, objects are detected *on their corresponding network scales*, which is more elegant and easier to optimize. (b) Negative space mining. The ratio between object and non-object samples is seriously imbalanced. So an object detector should have effective negative mining strategies [@ohem]. In order to reduce the searching space of the objects, we create an objectness prior (Figure \[pipline\]) on convolutional feature maps and jointly optimize it with the detector at training phase.

As a result, we propose RON (Reverse connection with Objectness prior Networks) 