  The semantic segmentation (SS) task aims to create a dense classification by
labeling at the pixel level each object present on images. Convolutional neural
network (CNN) approaches have been widely used, and exhibited the best results
in this task. However, the loss of spatial precision on the results is a main
drawback that has not been solved. In this work, we propose to use a multi-task
approach by complementing the semantic segmentation task with edge detection,
semantic contour, and distance transform tasks. We propose that by sharing a
common latent space, the complementary tasks can produce more robust
representations that can enhance the semantic labels. We explore the influence
of contour-based tasks on latent space, as well as their impact on the final
results of SS. We demonstrate the effectiveness of learning in a multi-task
setting for hourglass models in the Cityscapes, CamVid, and Freiburg Forest
datasets by improving the state-of-the-art without any refinement
post-processing.
