arXiv:2410.08692v1 Announce Type: new
Abstract: Multimodal sentiment analysis (MSA) systems leverage information from different modalities to predict human sentiment intensities. Incomplete modality is an important issue that may cause a significant performance drop in MSA systems. By generative imputation, i.e., recovering the missing data from available data, systems may achieve robust performance but will lead to high computational costs. This paper introduces a knowledge distillation method, called `Multi-Modal Contrastive Knowledge Distillation’ (MM-CKD), to address the issue of incomplete modality in video sentiment analysis with lower computation cost, as a novel non-imputation-based method. We employ Multi-view Supervised Contrastive Learning (MVSC) to transfer knowledge from a teacher model to student models. This approach not only leverages cross-modal knowledge but also introduces cross-sample knowledge with supervision, jointly improving the performance of both teacher and student models through online learning. Our method gives competitive results with significantly lower computational costs than state-of-the-art imputation-based methods.
Analysis of Multi-Modal Contrastive Knowledge Distillation in Video Sentiment Analysis
In the field of multimedia information systems, the analysis and understanding of human sentiment in various forms of media have gained significant attention. Sentiment analysis can help researchers and practitioners identify and analyze emotions expressed by individuals, which is valuable for applications like marketing, user feedback analysis, and content recommendation systems. In the context of multimedia, sentiment analysis often involves leveraging information from different modalities, such as text, audio, and visual cues, to predict sentiment intensities accurately. This is known as multimodal sentiment analysis (MSA).
One major challenge in MSA is dealing with incomplete modality, where one or more modalities may be missing or unavailable in a given dataset. Incomplete modality can significantly affect the performance of MSA systems, as crucial information may be lost. To address this issue, researchers have previously employed generative imputation methods that recover missing data from the available data. While these methods can improve performance, they come with high computational costs.
With the aim of mitigating the limitations of imputation-based methods, this paper introduces a novel approach called Multi-Modal Contrastive Knowledge Distillation (MM-CKD). Knowledge distillation is a concept that involves transferring knowledge from a large, well-performing model (the “teacher”) to a smaller, more efficient model (the “student”). In the case of MM-CKD, the knowledge transfer is performed in a cross-modal and cross-sample manner, leveraging multi-view supervised contrastive learning.
The MM-CKD method presented in this paper offers several advantages. Firstly, it tackles the challenge of incomplete modality without relying on data imputation, thus avoiding the associated computational costs. Secondly, it leverages both cross-modal and cross-sample knowledge to improve the performance of both the teacher and student models. This joint learning process enhances the understanding of sentiment analysis in videos. Lastly, the experimental results demonstrate that MM-CKD achieves competitive performance compared to state-of-the-art imputation-based methods while requiring significantly lower computational resources.
This research highlights the multi-disciplinary nature of multimedia information systems. It combines concepts from sentiment analysis, generative modeling, knowledge distillation, and contrastive learning. By integrating these diverse methodologies, the authors have shown how to address the challenge of incomplete modality in video sentiment analysis effectively.
In the broader context of multimedia information systems, the findings of this research contribute to advancements in various domains. Firstly, the development of more efficient and accurate multimodal sentiment analysis techniques can improve user experience in applications such as content recommendation systems and personalized advertising. Secondly, the knowledge distillation approach demonstrated in this paper can be applied to other multimedia tasks, such as object recognition, activity recognition, and video summarization. Lastly, the use of contrastive learning can enhance our understanding of the relationships between different modalities in multimedia data, leading to further insights and developments in the field of artificial reality, augmented reality, and virtual realities.