Title: PartCo: Part-Level Correspondence Priors Enhance Category Discovery
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Generalized Category Discovery

Abstract: Generalized Category Discovery (GCD) aims to identify both known and novel categories within unlabeled data by leveraging a set of labeled examples from known categories. Existing GCD methods primarily depend on semantic labels and global image representations, often overlooking the detailed part-level cues that are crucial for distinguishing closely related categories. In this paper, we introduce PartCo, short for Part-Level Correspondence Prior, a novel framework that enhances category discovery by incorporating part-level visual feature correspondences. By leveraging part-level relationships, PartCo captures finer-grained semantic structures, enabling a more nuanced understanding of category relationships. Importantly, PartCo seamlessly integrates with existing GCD methods without requiring significant modifications. Our extensive experiments on multiple benchmark datasets demonstrate that PartCo significantly improves the performance of current GCD approaches, achieving state-of-the-art results by bridging the gap between semantic labels and part-level visual compositions, thereby setting new benchmarks for GCD. Code will be made publicly available.

TL;DR: We present PartCo, a framework that uses part-level visual cues to enhance Generalized Category Discovery, achieving state-of-the-art performance with seamless integration into existing methods.