  Real-world applications have high demands for semantic segmentation methods.
Although semantic segmentation has made remarkable leap-forwards with deep
learning, the performance of real-time methods is not satisfactory. In this
work, we propose PP-LiteSeg, a novel lightweight model for the real-time
semantic segmentation task. Specifically, we present a Flexible and Lightweight
Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen
feature representations, we propose a Unified Attention Fusion Module (UAFM),
which takes advantage of spatial and channel attention to produce a weight and
then fuses the input features with the weight. Moreover, a Simple Pyramid
Pooling Module (SPPM) is proposed to aggregate global context with low
computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a
superior trade-off between accuracy and speed compared to other methods. On the
Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5%
mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at
PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.
