  The encoder-decoder architecture is widely used as a lightweight semantic
segmentation network. However, it struggles with a limited performance compared
to a well-designed Dilated-FCN model for two major problems. First, commonly
used upsampling methods in the decoder such as interpolation and deconvolution
suffer from a local receptive field, unable to encode global contexts. Second,
low-level features may bring noises to the network decoder through skip
connections for the inadequacy of semantic concepts in early encoder layers. To
tackle these challenges, a Global Enhancement Method is proposed to aggregate
global information from high-level feature maps and adaptively distribute them
to different decoder layers, alleviating the shortage of global contexts in the
upsampling process. Besides, a Local Refinement Module is developed by
utilizing the decoder features as the semantic guidance to refine the noisy
encoder features before the fusion of these two (the decoder features and the
encoder features). Then, the two methods are integrated into a Context Fusion
Block, and based on that, a novel Attention guided Global enhancement and Local
refinement Network (AGLN) is elaborately designed. Extensive experiments on
PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the
effectiveness of the proposed approach. In particular, with a vanilla
ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean
IoU) on the PASCAL Context dataset. The code is available at
https://github.com/zhasen1996/AGLN.
