Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text
representations that have found numerous applications, from zero-shot classification to text …

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 …

Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

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 …

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 this paper, we study the problem of Generalized Category Discovery (GCD), which aims to
cluster unlabeled data from both known and unknown categories using the knowledge of …

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 …