  We address the problem of generalized category discovery (GCD) that aims to
partition a partially labeled collection of images; only a small part of the
collection is labeled and the total number of target classes is unknown. To
address this generalized image clustering problem, we revisit the mean-shift
algorithm, i.e., a classic, powerful technique for mode seeking, and
incorporate it into a contrastive learning framework. The proposed method,
dubbed Contrastive Mean-Shift (CMS) learning, trains an image encoder to
produce representations with better clustering properties by an iterative
process of mean shift and contrastive update. Experiments demonstrate that our
method, both in settings with and without the total number of clusters being
known, achieves state-of-the-art performance on six public GCD benchmarks
without bells and whistles.
