arXiv:2405.12775v1 Announce Type: new
Abstract: 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.
Understanding Multimodal Utterances with Unsupervised Multimodal Clustering (UMC)
In the field of multimedia information systems, understanding and analyzing multimodal utterances is crucial for enhancing human-machine interactions. Multimodal utterances consist of both verbal and nonverbal information, such as spoken words, facial expressions, gestures, and more. Traditional methods for discerning complex semantics in unsupervised scenarios have struggled to effectively leverage this nonverbal information.
This new research paper introduces a novel unsupervised multimodal clustering method called UMC, which makes significant strides in this field. UMC takes a unique approach to constructing augmentation views for multimodal data, allowing for pre-training and the establishment of well-initialized representations for subsequent clustering.
One of the key innovations of UMC is its strategy for dynamically selecting high-quality samples as guidance for representation learning. This selection process is based on the density of each sample’s nearest neighbors. By focusing on high-quality samples, UMC is able to refine the learning process and improve the overall clustering results.
In addition, UMC is equipped with the capability to automatically determine the optimal value for the top-K parameter in each cluster. This refinement further enhances the sample selection process and ensures that the clustering is performed as effectively as possible.
The authors of the paper evaluated UMC using benchmark multimodal intent and dialogue act datasets. The results showed remarkable improvements of 2-6% scores in clustering metrics compared to state-of-the-art methods. This marks a significant achievement in the field and highlights the potential of UMC for advancing our understanding of multimodal utterances.
The concepts presented in this paper go beyond the realm of multimodal clustering and have implications for various disciplines within the field of multimedia information systems. Animations, artificial reality, augmented reality, and virtual realities are all heavily reliant on effective understanding and synthesis of multimodal data. The advancements made by UMC in unsupervised semantic clustering can have a profound impact on the development of more immersive and interactive multimedia experiences.
In conclusion, this paper introduces UMC, a groundbreaking unsupervised multimodal clustering method that significantly improves the understanding of multimodal utterances. The innovative approaches employed by UMC, such as constructing augmentation views and dynamically selecting high-quality samples, pave the way for more effective and accurate clustering in unsupervised scenarios. The application of UMC extends beyond clustering and has implications for various disciplines within the wider field of multimedia information systems.
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