  The low-level details and high-level semantics are both essential to the
semantic segmentation task. However, to speed up the model inference, current
approaches almost always sacrifice the low-level details, which leads to a
considerable accuracy decrease. We propose to treat these spatial details and
categorical semantics separately to achieve high accuracy and high efficiency
for realtime semantic segmentation. To this end, we propose an efficient and
effective architecture with a good trade-off between speed and accuracy, termed
Bilateral Segmentation Network (BiSeNet V2). This architecture involves: (i) a
Detail Branch, with wide channels and shallow layers to capture low-level
details and generate high-resolution feature representation; (ii) a Semantic
Branch, with narrow channels and deep layers to obtain high-level semantic
context. The Semantic Branch is lightweight due to reducing the channel
capacity and a fast-downsampling strategy. Furthermore, we design a Guided
Aggregation Layer to enhance mutual connections and fuse both types of feature
representation. Besides, a booster training strategy is designed to improve the
segmentation performance without any extra inference cost. Extensive
quantitative and qualitative evaluations demonstrate that the proposed
architecture performs favourably against a few state-of-the-art real-time
semantic segmentation approaches. Specifically, for a 2,048x1,024 input, we
achieve 72.6% Mean IoU on the Cityscapes test set with a speed of 156 FPS on
one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than
existing methods, yet we achieve better segmentation accuracy.
