Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Text Classification problem has been thoroughly studied in information retrieval problems
and data mining tasks. It is beneficial in multiple tasks including medical diagnose health …

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To
better comprehend long complicated sentences and obtain accurate aspect-specific …

As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis
(MABSA) has attracted increasing attention in recent years. However, previous approaches …

Abstract Multi-modal (vision-language) models, such as CLIP, are replacing traditional
supervised pre-training models (eg, ImageNet-based pre-training) as the new generation of …

Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which aims to
generate a natural language sentence for a multimodal social post (an image as well as its …

Multimodal abstractive summarization (MAS) aims to produce a concise summary given the
multimodal data (text and vision). Existing studies mainly focus on how to effectively use the …

Sentiment analysis is a crucial task that aims to understand people's emotional states and
predict emotional categories based on multimodal information. It consists of several …

Multimodal learning has been a field of increasing interest, aiming to combine various
modalities in a single joint representation. Especially in the area of visiolinguistic (VL) …