  Discovering the semantics of multimodal utterances is essential for
understanding human language and enhancing human-machine interactions. Existing
methods manifest limitations in leveraging nonverbal information for discerning
complex semantics in unsupervised scenarios. This paper introduces a novel
unsupervised multimodal clustering method (UMC), making a pioneering
contribution to this field. UMC introduces a unique approach to constructing
augmentation views for multimodal data, which are then used to perform
pre-training to establish well-initialized representations for subsequent
clustering. An innovative strategy is proposed to dynamically select
high-quality samples as guidance for representation learning, gauged by the
density of each sample's nearest neighbors. Besides, it is equipped to
automatically determine the optimal value for the top-$K$ parameter in each
cluster to refine sample selection. Finally, both high- and low-quality samples
are used to learn representations conducive to effective clustering. We build
baselines on benchmark multimodal intent and dialogue act datasets. UMC shows
remarkable improvements of 2-6\% scores in clustering metrics over
state-of-the-art methods, marking the first successful endeavor in this domain.
The complete code and data are available at https://github.com/thuiar/UMC.
