  Multi-view clustering (MVC) has had significant implications in cross-modal
representation learning and data-driven decision-making in recent years. It
accomplishes this by leveraging the consistency and complementary information
among multiple views to cluster samples into distinct groups. However, as
contrastive learning continues to evolve within the field of computer vision,
self-supervised learning has also made substantial research progress and is
progressively becoming dominant in MVC methods. It guides the clustering
process by designing proxy tasks to mine the representation of image and video
data itself as supervisory information. Despite the rapid development of
self-supervised MVC, there has yet to be a comprehensive survey to analyze and
summarize the current state of research progress. Therefore, this paper
explores the reasons and advantages of the emergence of self-supervised MVC and
discusses the internal connections and classifications of common datasets, data
issues, representation learning methods, and self-supervised learning methods.
This paper does not only introduce the mechanisms for each category of methods
but also gives a few examples of how these techniques are used. In the end,
some open problems are pointed out for further investigation and development.
