  In this paper we propose an improved mean-field inference algorithm for the
fully connected paired CRFs model. The improved method Message Passing
operation is changed from the original linear convolution to the present graph
attention operation, while the process of the inference algorithm is turned
into the forward process of the GAT model. Combined with the mean-field
inferred label distribution, it is equivalent to the output of a classifier
with only unary potential. To this end, we propose a graph attention network
model with residual structure, and the model approach is applicable to all
sequence annotation tasks, such as pixel-level image semantic segmentation
tasks as well as text annotation tasks.

Machine Learning;Artificial Intelligence