Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and
unseen classes, when only the labeled examples from seen classes are provided. Recent …

Metric learning aims to learn a highly discriminative model encouraging the embeddings of
similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The …

For image segmentation, the current standard is to perform pixel-level optimization and
inference in Euclidean output embedding spaces through linear hyperplanes. In this work …

Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …

Although self-/un-supervised methods have led to rapid progress in visual representation
learning, these methods generally treat objects and scenes using the same lens. In this …

Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics
based on the training of data with seen semantics. Recent studies either leverage the global …

Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation
ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to …

Deep representation learning is a ubiquitous part of modern computer vision. While
Euclidean space has been the de facto standard manifold for learning visual …

Few-shot learning describes the challenging problem of recognizing samples from unseen
classes given very few labeled examples. In many cases, few-shot learning is cast as …