In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically,
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that 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 …

We explore the problem of Incremental Generalized Category Discovery (IGCD). This is a
challenging category-incremental learning setting where the goal is to develop models that …

Abstract Generalized Category Discovery (GCD) is a pragmatic and challenging open-world
task which endeavors to cluster unlabeled samples from both novel and old classes …

In the quest for unveiling novel categories at test time, we confront the inherent limitations of
traditional supervised recognition models that are restricted by a predefined category set …

Visual category discovery methods aim to find novel categories in unlabeled visual data. At
training time, a set of labeled and unlabeled images are provided, where the labels …

Abstract Generalized Category Discovery (GCD) is a recently proposed open-world problem
that aims to automatically discover and cluster based on partially labeled data. The …

In this paper, we address a complex but practical scenario in Active Learning (AL) known as
open-set AL, where the unlabeled data consists of both in-distribution (ID) and out-of …

Data imbalance and open-ended distribution are two intrinsic characteristics of the real
visual world. Though encouraging progress has been made in tackling each challenge …