  Contextual information has been shown to be powerful for semantic
segmentation. This work proposes a novel Context-based Tandem Network (CTNet)
by interactively exploring the spatial contextual information and the channel
contextual information, which can discover the semantic context for semantic
segmentation. Specifically, the Spatial Contextual Module (SCM) is leveraged to
uncover the spatial contextual dependency between pixels by exploring the
correlation between pixels and categories. Meanwhile, the Channel Contextual
Module (CCM) is introduced to learn the semantic features including the
semantic feature maps and class-specific features by modeling the long-term
semantic dependence between channels. The learned semantic features are
utilized as the prior knowledge to guide the learning of SCM, which can make
SCM obtain more accurate long-range spatial dependency. Finally, to further
improve the performance of the learned representations for semantic
segmentation, the results of the two context modules are adaptively integrated
to achieve better results. Extensive experiments are conducted on three
widely-used datasets, i.e., PASCAL-Context, ADE20K and PASCAL VOC2012. The
results demonstrate the superior performance of the proposed CTNet by
comparison with several state-of-the-art methods.
