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.

本文提出了一种名为 CCL 的基于聚类的对比学习方法，利用聚类得到的标签和视频约束来学习具有区分性的人脸特征，展示了该方法在视频人脸聚类的表征学习任务上的显著成果。

基于聚类的对比学习，以提高人脸表征

Clustering based Contrastive Learning for Improving Face Representations

Analyzing the story behind TV series and movies often requires understanding
who the characters are and what they are doing. With improving deep face
models, this may seem like a solved problem. However, as face detectors get
better, clustering/identification needs to be revisited to address increasing
diversity in facial appearance. In this paper, we address video face clustering
using unsupervised methods. Our emphasis is on distilling the essential
information, identity, from the representations obtained using deep pre-trained
face networks. We propose a self-supervised Siamese network that can be trained
without the need for video/track based supervision, and thus can also be
applied to image collections. We evaluate our proposed method on three video
face clustering datasets. The experiments show that our methods outperform
current state-of-the-art methods on all datasets. Video face clustering is
lacking a common benchmark as current works are often evaluated with different
metrics and/or different sets of face tracks.

本文提出了一种无监督方法，使用基于深度预训练人脸网络获得的表示来提取视频中的身份信息，为此，我们设计了一种自监督的 Siamese 网络，并在三个视频数据集上进行了评估，结果表明我们的方法优于当前所有现有的方法。