Contextuality Helps Representation Learning for Generalized Category Discovery

TL;DR

This paper presents a novel dual-context approach leveraging contextuality at instance and cluster levels to improve generalized category discovery, outperforming existing methods on benchmark datasets.

Contribution

It introduces a dual-context method for GCD that enhances feature learning by integrating instance-level and cluster-level contextual information.

Findings

Outperforms state-of-the-art on benchmark datasets.

Effectively handles scenarios with both known and novel categories.

Improves classification accuracy through contextual feature learning.

Abstract

This paper introduces a novel approach to Generalized Category Discovery (GCD) by leveraging the concept of contextuality to enhance the identification and classification of categories in unlabeled datasets. Drawing inspiration from human cognition's ability to recognize objects within their context, we propose a dual-context based method. Our model integrates two levels of contextuality: instance-level, where nearest-neighbor contexts are utilized for contrastive learning, and cluster-level, employing prototypical contrastive learning based on category prototypes. The integration of the contextual information effectively improves the feature learning and thereby the classification accuracy of all categories, which better deals with the real-world datasets. Different from the traditional semi-supervised and novel category discovery techniques, our model focuses on a more realistic and challenging scenario where both known and novel categories are present in the unlabeled data. Extensive experimental results on several benchmark data sets demonstrate that the proposed model outperforms the state-of-the-art. Code is available at: https://github.com/Clarence-CV/Contexuality-GCD