  We propose a hybrid architecture composed of a fully convolutional network
(FCN) and a Dempster-Shafer layer for image semantic segmentation. In the
so-called evidential FCN (E-FCN), an encoder-decoder architecture first
extracts pixel-wise feature maps from an input image. A Dempster-Shafer layer
then computes mass functions at each pixel location based on distances to
prototypes. Finally, a utility layer performs semantic segmentation from mass
functions and allows for imprecise classification of ambiguous pixels and
outliers. We propose an end-to-end learning strategy for jointly updating the
network parameters, which can make use of soft (imprecise) labels. Experiments
using three databases (Pascal VOC 2011, MIT-scene Parsing and SIFT Flow) show
that the proposed combination improves the accuracy and calibration of semantic
segmentation by assigning confusing pixels to multi-class sets.
