Deep networks have strong capacities of embedding data into latent representations and
finishing following tasks. However, the capacities largely come from high-quality annotated …

In this paper, we consider a highly general image recognition setting wherein, given a
labelled and unlabelled set of images, the task is to categorize all images in the unlabelled …

Recent self-supervised methods for image representation learning are based on maximizing
the agreement between embedding vectors from different views of the same image. A trivial …

In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring
novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled …

Unsupervised object re-identification targets at learning discriminative representations for
object retrieval without any annotations. Clustering-based methods conduct training with the …

In this paper, we address the problem of generalized category discovery (GCD), ie, given a
set of images where part of them are labelled and the rest are not, the task is to automatically …

In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in
a set of unlabeled samples given a labeled dataset with known classes. We exploit the …

We consider the problem of discovering novel object categories in an image collection.
While these images are unlabelled, we also assume prior knowledge of related but different …

Semi-supervised learning (SSL) is one of the dominant approaches to address the
annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage …

We tackle the problem of discovering novel classes in an image collection given labelled
examples of other classes. We present a new approach called AutoNovel to address this …