Gated additive skip context connection for object detection

Abstract

Context information plays an important role in object detection. DeepID concatenates the global context for classification, while Yolo, SSD, and Crafting use local context information for detection. In this paper, we propose a straightforward method to plug the global context information into the Faster RCNN Framework, namely the Skip Context Connection (SCC). We use SCC to inject the global context into the object representation which skips the RoiPooling layer rather than drops it. Therefore, it can not only leverage the context information but also keep the location accuracy from the RCNN framework. We proposed three principles to construct the SCC blocks: effectiveness means fewer parameters, additivity means the features possess the same meaning, and selectable means soft gated addition. We also evaluate several different SCC blocks. The Gated Additive SCC(GA-SCC) which satisfy the three principles get the best performance. Our experiment results on PASCAL VOC 2007 show that GA-SCC can get the steady 1% improvement over the traditional RCNN method.