CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection (2204.05515v4)

Abstract: Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data. Previous research works rarely consider token-level feature fusion, and few works explore learning the common features related to sentiment in multimodal data to help the model fuse multimodal features. In this paper, we propose a Contrastive Learning and Multi-Layer Fusion (CLMLF) method for multimodal sentiment detection. Specifically, we first encode text and image to obtain hidden representations, and then use a multi-layer fusion module to align and fuse the token-level features of text and image. In addition to the sentiment analysis task, we also designed two contrastive learning tasks, label based contrastive learning and data based contrastive learning tasks, which will help the model learn common features related to sentiment in multimodal data. Extensive experiments conducted on three publicly available multimodal datasets demonstrate the effectiveness of our approach for multimodal sentiment detection compared with existing methods. The codes are available for use at https://github.com/Link-Li/CLMLF

An Examination of "CLMLF: A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection"

The paper "CLMLF: A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection" presents a novel approach to enhancing multimodal sentiment analysis by leveraging contrastive learning techniques and a multi-layer fusion framework. Authored by researchers from Harbin Institute of Technology, this paper explores the domain of multimodal deep learning, which involves analyzing sentiment using both text and image data — a significant enhancement over traditional unimodal approaches.

Overview and Methodology

The authors propose the CLMLF (Contrastive Learning and Multi-Layer Fusion) model, which innovatively employs contrastive learning alongside a Transformer-based multi-layer fusion framework. The primary advancements presented include:

1. Multimodal Feature Fusion: The paper highlights a significant gap in existing research regarding token-level feature fusion and alignment. CLMLF addresses this by encoding text and image data separately using BERT and ResNet, respectively, followed by a multi-layer fusion module. This module uses a Transformer-Encoder to align and fuse features at the token level, enhancing the model’s ability to correctly interpret multimodal sentiment.
2. Contrastive Learning Tasks: The authors introduce two specific contrastive learning tasks to complement the primary sentiment analysis task — Label Based Contrastive Learning (LBCL) and Data Based Contrastive Learning (DBCL). LBCL aims to reinforce the model’s understanding of label similarity across the dataset, while DBCL employs data augmentation to refine the model's understanding of invariant features within different modalities.
3. Dataset Evaluation: To validate their approach, the authors deploy experiments on three publicly available multimodal sentiment analysis datasets: MVSA-Single, MVSA-Multiple, and HFM. The CLMLF model demonstrates superior performance against several baselines across these datasets, notably excelling in datasets where multimodal data significantly aid sentiment understanding.

Results and Implications

Quantitative results showcased in the paper offer several insights. The CLMLF model significantly outperforms previous multimodal sentiment analysis approaches, a performance attributed to the tailored use of token-level alignment and contrastive learning. Specifically, CLMLF achieved an accuracy of 75.33% and an F1 score of 73.46% on the MVSA-Single dataset, surpassing existing models by a noticeable margin.

Practically, this research advances sentiment analysis applications on social platforms where users express opinions both verbally and visually. Theoretically, it underscores the importance of effective multimodal fusion techniques and contrast learning tasks, suggesting these could be integral to the next wave of enhancements in sentiment detection.

Speculation and Future Directions

The integration of contrastive learning with multimodal data fusion is a promising path likely to influence a variety of AI fields beyond sentiment analysis, such as visual question answering and cross-modal retrieval. The architecture of CLMLF, which combines the strengths of multimodal data with advanced learning techniques, may inspire further exploration into multimodal encoding and alignment strategies. Future work could extend this framework to include additional modalities like audio, thus broadening its applicability in AI-driven sentiment interpretation and analysis.

In conclusion, the paper "CLMLF: A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection" provides a substantive contribution to the discourse on enhancing sentiment detection through effective multimodal approaches. It paves the way for deeper exploration into contrastive learning's role in improving models’ interpretative capacity across diverse modalities.