  This paper presents a novel method for face clustering in videos using a
video-centralised transformer. Previous works often employed contrastive
learning to learn frame-level representation and used average pooling to
aggregate the features along the temporal dimension. This approach may not
fully capture the complicated video dynamics. In addition, despite the recent
progress in video-based contrastive learning, few have attempted to learn a
self-supervised clustering-friendly face representation that benefits the video
face clustering task. To overcome these limitations, our method employs a
transformer to directly learn video-level representations that can better
reflect the temporally-varying property of faces in videos, while we also
propose a video-centralised self-supervised framework to train the transformer
model. We also investigate face clustering in egocentric videos, a
fast-emerging field that has not been studied yet in works related to face
clustering. To this end, we present and release the first large-scale
egocentric video face clustering dataset named EasyCom-Clustering. We evaluate
our proposed method on both the widely used Big Bang Theory (BBT) dataset and
the new EasyCom-Clustering dataset. Results show the performance of our
video-centralised transformer has surpassed all previous state-of-the-art
methods on both benchmarks, exhibiting a self-attentive understanding of face
videos.
