Multi-Label Generalized Category Discovery

In this paper, we introduce Multi-Label Generalized Category Discovery (ML-GCD), a new setting for jointly recognising Known categories and discovering Novel categories in multi-label data. It extends the single-label formulation of Generalized Category Discovery (GCD) to category-wise presence estimation. In ML-GCD, labelled instances contain only Known categories, whereas the unlabelled pool spans Known-only, Novel-only, and mixed instances with all annotations withheld. For unlabelled instances, cross-view category learning derives supervision from model-generated targets, but the resulting consistency objective lacks an independent, fixed reference for representation learning. Our key idea is to couple category discovery with the preservation of global and local visual structure, using a fixed pretrained visual reference independent of evolving category assignments. We propose Patch-guided Discovery (PaD), which transfers Known-category knowledge to unlabelled instances, preserves global feature relations, and retains local visual information. Experiments on VOC2007, MIRFlickr, and COCO2014 demonstrate the effectiveness of PaD for ML-GCD, with ablation studies supporting the value of fixed visual guidance in complementing model-generated supervision.