  The convolutional neural network (CNN) has become a basic model for solving
many computer vision problems. In recent years, a new class of CNNs, recurrent
convolution neural network (RCNN), inspired by abundant recurrent connections
in the visual systems of animals, was proposed. The critical element of RCNN is
the recurrent convolutional layer (RCL), which incorporates recurrent
connections between neurons in the standard convolutional layer. With
increasing number of recurrent computations, the receptive fields (RFs) of
neurons in RCL expand unboundedly, which is inconsistent with biological facts.
We propose to modulate the RFs of neurons by introducing gates to the recurrent
connections. The gates control the amount of context information inputting to
the neurons and the neurons' RFs therefore become adaptive. The resulting layer
is called gated recurrent convolution layer (GRCL). Multiple GRCLs constitute a
deep model called gated RCNN (GRCNN). The GRCNN was evaluated on several
computer vision tasks including object recognition, scene text recognition and
object detection, and obtained much better results than the RCNN. In addition,
when combined with other adaptive RF techniques, the GRCNN demonstrated
competitive performance to the state-of-the-art models on benchmark datasets
for these tasks. The codes are released at
\href{https://github.com/Jianf-Wang/GRCNN}{https://github.com/Jianf-Wang/GRCNN}.
