  Two factors have proven to be very important to the performance of semantic
segmentation models: global context and multi-level semantics. However,
generating features that capture both factors always leads to high
computational complexity, which is problematic in real-time scenarios. In this
paper, we propose a new model, called Attention-Augmented Network (AttaNet), to
capture both global context and multilevel semantics while keeping the
efficiency high. AttaNet consists of two primary modules: Strip Attention
Module (SAM) and Attention Fusion Module (AFM). Viewing that in challenging
images with low segmentation accuracy, there are a significantly larger amount
of vertical strip areas than horizontal ones, SAM utilizes a striping operation
to reduce the complexity of encoding global context in the vertical direction
drastically while keeping most of contextual information, compared to the
non-local approaches. Moreover, AFM follows a cross-level aggregation strategy
to limit the computation, and adopts an attention strategy to weight the
importance of different levels of features at each pixel when fusing them,
obtaining an efficient multi-level representation. We have conducted extensive
experiments on two semantic segmentation benchmarks, and our network achieves
different levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9%
mIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on
ADE20K as well.
