Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances
<|reference_start|>Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances: 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.<|reference_end|>
@article{zhang2024unsupervised,
    title={Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal
  Utterances},
    author={Hanlei Zhang, Hua Xu, Fei Long, Xin Wang, Kai Gao},
    journal={arXiv preprint arXiv:2405.12775},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12775},
    primaryClass={cs.MM cs.AI cs.CL}
}