  In this paper, we present ShelfNet, a novel architecture for accurate fast
semantic segmentation. Different from the single encoder-decoder structure,
ShelfNet has multiple encoder-decoder branch pairs with skip connections at
each spatial level, which looks like a shelf with multiple columns. The
shelf-shaped structure can be viewed as an ensemble of multiple deep and
shallow paths, thus improving accuracy. We significantly reduce computation
burden by reducing channel number, at the same time achieving high accuracy
with this unique structure. In addition, we propose a shared-weight strategy in
the residual block which reduces parameter number without sacrificing
performance. Compared with popular non real-time methods such as PSPNet, our
ShelfNet achieves 4$\times$ faster inference speed with similar accuracy on
PASCAL VOC dataset. Compared with real-time segmentation models such as
BiSeNet, our model achieves higher accuracy at comparable speed on the
Cityscapes Dataset, enabling the application in speed-demanding tasks such as
street-scene understanding for autonomous driving. Furthermore, our ShelfNet
achieves 79.0\% mIoU on Cityscapes Dataset with ResNet34 backbone,
outperforming PSPNet and BiSeNet with large backbones such as ResNet101.
Through extensive experiments, we validated the superior performance of
ShelfNet. We provide link to the implementation
\url{https://github.com/juntang-zhuang/ShelfNet-lw-cityscapes}.

Computer Vision and Pattern Recognition