Clustering based Contrastive Learning for Improving Face Representations
This work addresses video face clustering, a domain-specific task in computer vision, with incremental improvements over existing methods.
The paper tackled the problem of learning discriminative face features for video face clustering by introducing Clustering-based Contrastive Learning (CCL), which uses clustering-derived labels and video constraints, achieving a new state-of-the-art on three challenging datasets: BBT-0101, BF-0502, and ACCIO.
A good clustering algorithm can discover natural groupings in data. These groupings, if used wisely, provide a form of weak supervision for learning representations. In this work, we present Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that uses labels obtained from clustering along with video constraints to learn discriminative face features. We demonstrate our method on the challenging task of learning representations for video face clustering. Through several ablation studies, we analyze the impact of creating pair-wise positive and negative labels from different sources. Experiments on three challenging video face clustering datasets: BBT-0101, BF-0502, and ACCIO show that CCL achieves a new state-of-the-art on all datasets.