Deep embeddings answer one simple question: How similar are two images? Learning
these embeddings is the bedrock of verification, zero-shot learning, and visual search. The …

Human vision is able to immediately recognize novel visual categories after seeing just one
or a few training examples. We describe how to add a similar capability to ConvNet …

We address the problem of distance metric learning (DML), defined as learning a distance
consistent with a notion of semantic similarity. Traditionally, for this problem supervision is …

The modern image search system requires semantic understanding of image, and a key yet
under-addressed problem is to learn a good metric for measuring the similarity between …

To solve deep metric learning problems and produce feature embeddings, current
methodologies will commonly use a triplet model to minimise the relative distance between …

We propose a new supervized learning framework for oversegmenting 3D point clouds into
superpoints. We cast this problem as learning deep embeddings of the local geometry and …

Few-shot learning refers to understanding new concepts from only a few examples. We
propose an information retrieval-inspired approach for this problem that is motivated by the …

While deep neural networks have demonstrated competitive results for many visual
recognition and image retrieval tasks, the major challenge lies in distinguishing similar …

We introduce an information-theoretic loss function, RankMI, and an associated training
algorithm for deep representation learning for image retrieval. Our proposed framework …

State-of-the-art deep neural network recognition systems are designed for a static and
closed world. It is usually assumed that the distribution at test time will be the same as the …