Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Recently, multimodal fusion efforts have achieved remarkable success in Multimodal
Sentiment Analysis (MSA). However, most of the existing methods are based on model-level …

Vision-language models (VLMs) mainly rely on contrastive training to learn general-purpose
representations of images and captions. We focus on the situation when one image is …

This paper explores a novel multi-modal alternating learning paradigm pursuing a
reconciliation between the exploitation of uni-modal features and the exploration of cross …

Contrastive learning methods, such as CLIP, leverage naturally paired data—for example,
images and their corresponding text captions—to learn general representations that transfer …