Images and text have become essential parts of the multimodal machine learning (MMML)
framework in today's world because data are always available, and technological …

The spread of altered media in the form of fake videos, audios, and images, has been
largely increased over the past few years. Advanced digital manipulation tools and …

Multimodal sentiment analysis (MSA) is the process of identifying sentiment polarities that
users may simultaneously display in text, audio, and video data. Sentiment analysis …

Investigating interactions between image and text can effectively improve image–text
sentiment analysis, but most existing methods do not explore image–text interaction at fine …

Compared with single-modal content, multimodal content conveys user's sentiments and
feelings more vividly. Thus, multimodal sentiment analysis has become a research hotspot …

As the business world shifts to the web and tremendous amounts of data become available
on multilingual mobile applications, new business and research challenges and …

Advancements in Multimodal Sentiment Analysis (MSA) have predominantly focused on
leveraging the interdependence of text, acoustic, and visual modalities to enhance …

Deep neural networks (DNNs) are rapidly evolving from streamlined single-modality single-
task (SMST) to multimodality multitask (MMMT) with large variations for different layers and …

Exploring the interaction between image and text has a great strength for image-text
sentiment analysis. However, most methods only focus on learning forward interaction in …

Recent machine learning (ML) models have advanced from single-modality single-task to
multi-modality multi-task (MMMT). MMMT models typically have multiple backbones of …