  Recent works attempt to improve scene parsing performance by exploring
different levels of contexts, and typically train a well-designed convolutional
network to exploit useful contexts across all pixels equally. However, in this
paper, we find that the context demands are varying from different pixels or
regions in each image. Based on this observation, we propose an Adaptive
Context Network (ACNet) to capture the pixel-aware contexts by a competitive
fusion of global context and local context according to different per-pixel
demands. Specifically, when given a pixel, the global context demand is
measured by the similarity between the global feature and its local feature,
whose reverse value can be used to measure the local context demand. We model
the two demand measurements by the proposed global context module and local
context module, respectively, to generate adaptive contextual features.
Furthermore, we import multiple such modules to build several adaptive context
blocks in different levels of network to obtain a coarse-to-fine result.
Finally, comprehensive experimental evaluations demonstrate the effectiveness
of the proposed ACNet, and new state-of-the-arts performances are achieved on
all four public datasets, i.e. Cityscapes, ADE20K, PASCAL Context, and COCO
Stuff.
