  The visual cues from multiple support regions of different sizes and
resolutions are complementary in classifying a candidate box in object
detection. Effective integration of local and contextual visual cues from these
regions has become a fundamental problem in object detection.
  In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass
messages among features from different support regions during both feature
learning and feature extraction. Such message passing can be implemented
through convolution between neighboring support regions in two directions and
can be conducted in various layers. Therefore, local and contextual visual
patterns can validate the existence of each other by learning their nonlinear
relationships and their close interactions are modeled in a more complex way.
It is also shown that message passing is not always helpful but dependent on
individual samples. Gated functions are therefore needed to control message
transmission, whose on-or-offs are controlled by extra visual evidence from the
input sample. The effectiveness of GBD-Net is shown through experiments on
three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
This paper also shows the details of our approach in wining the ImageNet object
detection challenge of 2016, with source code provided on
\url{https://github.com/craftGBD/craftGBD}.
