  Semantic boundary and edge detection aims at simultaneously detecting object
edge pixels in images and assigning class labels to them. Systematic training
of predictors for this task requires the labeling of edges in images which is a
particularly tedious task. We propose a novel strategy for solving this task,
when pixel-level annotations are not available, performing it in an almost
zero-shot manner by relying on conventional whole image neural net classifiers
that were trained using large bounding boxes. Our method performs the following
two steps at test time. Firstly it predicts the class labels by applying the
trained whole image network to the test images. Secondly, it computes
pixel-wise scores from the obtained predictions by applying backprop gradients
as well as recent visualization algorithms such as deconvolution and layer-wise
relevance propagation. We show that high pixel-wise scores are indicative for
the location of semantic boundaries, which suggests that the semantic boundary
problem can be approached without using edge labels during the training phase.
