  We propose a novel method for discovering shape regions that strongly
correlate with user-prescribed tags. For example, given a collection of chairs
tagged as either "has armrest" or "lacks armrest", our system correctly
highlights the armrest regions as the main distinctive parts between the two
chair types. To obtain point-wise predictions from shape-wise tags we develop a
novel neural network architecture that is trained with tag classification loss,
but is designed to rely on segmentation to predict the tag. Our network is
inspired by U-Net, but we replicate shallow U structures several times with new
skip connections and pooling layers, and call the resulting architecture
"WU-Net". We test our method on segmentation benchmarks and show that even with
weak supervision of whole shape tags, our method can infer meaningful semantic
regions, without ever observing shape segmentations. Further, once trained, the
model can process shapes for which the tag is entirely unknown. As a bonus, our
architecture is directly operational under full supervision and performs
strongly on standard benchmarks. We validate our method through experiments
with many variant architectures and prior baselines, and demonstrate several
applications.
