Tags2Parts: Discovering Semantic Regions from Shape Tags

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 is able to 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 network architecture is directly operational in a stronglysupervised scenario and outperforms state-of-the-art strongly-supervised methods on standard benchmarks.