  RoIPool/RoIAlign is an indispensable process for the typical two-stage object
detection algorithm, it is used to rescale the object proposal cropped from the
feature pyramid to generate a fixed size feature map. However, these cropped
feature maps of local receptive fields will heavily lose global context
information. To tackle this problem, we propose a novel end-to-end trainable
framework, called Global Context Aware (GCA) RCNN, aiming at assisting the
neural network in strengthening the spatial correlation between the background
and the foreground by fusing global context information. The core component of
our GCA framework is a context aware mechanism, in which both global feature
pyramid and attention strategies are used for feature extraction and feature
refinement, respectively. Specifically, we leverage the dense connection to
improve the information flow of the global context at different stages in the
top-down process of FPN, and further use the attention mechanism to refine the
global context at each level in the feature pyramid. In the end, we also
present a lightweight version of our method, which only slightly increases
model complexity and computational burden. Experimental results on COCO
benchmark dataset demonstrate the significant advantages of our approach.
