  It is generally accepted that one of the critical parts of current vision
algorithms based on deep learning and convolutional neural networks is the
annotation of a sufficient number of images to achieve competitive performance.
This is particularly difficult for semantic segmentation tasks since the
annotation must be ideally generated at the pixel level. Weakly-supervised
semantic segmentation aims at reducing this cost by employing simpler
annotations that, hence, are easier, cheaper and quicker to produce. In this
paper, we propose and assess a new weakly-supervised semantic segmentation
approach making use of a novel loss function whose goal is to counteract the
effects of weak annotations. To this end, this loss function comprises several
terms based on partial cross-entropy losses, being one of them the Centroid
Loss. This term induces a clustering of the image pixels in the object classes
under consideration, whose aim is to improve the training of the segmentation
network by guiding the optimization. The performance of the approach is
evaluated against datasets from two different industry-related case studies:
while one involves the detection of instances of a number of different object
classes in the context of a quality control application, the other stems from
the visual inspection domain and deals with the localization of images areas
whose pixels correspond to scene surface points affected by a specific sort of
defect. The detection results that are reported for both cases show that,
despite the differences among them and the particular challenges, the use of
weak annotations do not prevent from achieving a competitive performance level
for both.
