Learning Hierarchical Semantic Classification by Grounding on Consistent Image Segmentations

Hierarchical semantic classification requires the prediction of a taxonomy tree instead of a single flat level of the tree, where both accuracies at individual levels and consistency across levels matter. We can train classifiers for individual levels, which has accuracy but not consistency, or we can train only the finest level classification and infer higher levels, which has consistency but not accuracy. Our key insight is that hierarchical recognition should not be treated as multi-task classification, as each level is essentially a different task and they would have to compromise with each other, but be grounded on image segmentations that are consistent across semantic granularities. Consistency can in fact improve accuracy. We build upon recent work on learning hierarchical segmentation for flat-level recognition, and extend it to hierarchical recognition. It naturally captures the intuition that fine-grained recognition requires fine image segmentation whereas coarse-grained recognition requires coarse segmentation; they can all be integrated into one recognition model that drives fine-to-coarse internal visual parsing.Additionally, we introduce a Tree-path KL Divergence loss to enforce consistent accurate predictions across levels. Our extensive experimentation and analysis demonstrate our significant gains on predicting an accurate and consistent taxonomy tree.

Unsupervised Feature Learning with Emergent Data-Driven Prototypicality

Given an image set without any labels, our goal is to train a model that maps each image to a point in a feature space such that, not only proximity indicates visual similarity, but where it is located directly encodes how prototypical the image is according to the dataset. Our key insight is to perform unsupervised feature learning in hyperbolic instead of Euclidean space, where the distance between points still reflect image similarity, and yet we gain additional capacity for representing prototypicality with the location of the point: The closer it is to the origin, the more prototypical it is. The latter property is simply emergent from optimizing the usual metric learning objective: The image similar to many training instances is best placed at the center of corresponding points in Euclidean space, but closer to the origin in hyperbolic space. We propose an unsupervised feature learning algorithm in Hyperbolic space with sphere pACKing. HACK first generates uniformly packed particles in the Poincar\'e ball of hyperbolic space and then assigns each image uniquely to each particle. Images after congealing are regarded more typical of the dataset it belongs to. With our feature mapper simply trained to spread out training instances in hyperbolic space, we observe that images move closer to the origin with congealing, validating our idea of unsupervised prototypicality discovery. We demonstrate that our data-driven prototypicality provides an easy and superior unsupervised instance selection to reduce sample complexity, increase model generalization with atypical instances and robustness with typical ones.