Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learning to …

In this paper, we are the first to investigate category discovery within hyperbolic space. We propose a simple yet effective framework for learning hierarchy-aware representations and …

As a different modality, text information can provide complementary discriminative information, which motivates us to introduce it into the GCD task.However, the lack of class names for …

Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets, providing a more realistic setting for image recognition.Essentially, …

Generalized Category Discovery (GCD) is a classification task that aims to classify both base and novel classes in unlabeled images, using knowledge from a labeled dataset.

To address this issue, we introduce the novel paradigm of Domain Generalization in GCD (DG-GCD), where only source data is available for training, while the target domain—with a distinct …

Clustering methods based on self-supervised contrastive learning of image features, such as GCD,XCon,etc. XCon

Generalized Category Discovery (GCD) aims to recognize unlabeled images from known and novel classes by distinguishing novel classes from known ones, while also transferring …

Poster Federated Generalized Category Discovery Nan Pu · Wenjing Li · Xinyuan Ji · Yalan Qin · Nicu Sebe · Zhun Zhong

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