  Many successful learning targets such as minimizing dice loss and
cross-entropy loss have enabled unprecedented breakthroughs in segmentation
tasks. Beyond these semantic metrics, this paper aims to introduce location
supervision into semantic segmentation. Based on this idea, we present a
Location-aware Upsampling (LaU) that adaptively refines the interpolating
coordinates with trainable offsets. Then, location-aware losses are established
by encouraging pixels to move towards well-classified locations. An LaU is
offset prediction coupled with interpolation, which is trained end-to-end to
generate confidence score at each position from coarse to fine. Guided by
location-aware losses, the new module can replace its plain counterpart
(\textit{e.g.}, bilinear upsampling) in a plug-and-play manner to further boost
the leading encoder-decoder approaches. Extensive experiments validate the
consistent improvement over the state-of-the-art methods on benchmark datasets.
Our code is available at
https://github.com/HolmesShuan/Location-aware-Upsampling-for-Semantic-Segmentation
