A comprehensive survey on applications of transformers for deep learning tasks
S Islam, H Elmekki, A Elsebai, J Bentahar… - Expert Systems with …, 2023 - Elsevier
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
Knowledge graphs meet multi-modal learning: A comprehensive survey
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 …
semantic web community's exploration into multi-modal dimensions unlocking new avenues …
A Fine‐Tuned BERT‐Based Transfer Learning Approach for Text Classification
R Qasim, WH Bangyal, MA Alqarni… - Journal of healthcare …, 2022 - Wiley Online Library
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 …
and data mining tasks. It is beneficial in multiple tasks including medical diagnose health …
Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To
better comprehend long complicated sentences and obtain accurate aspect-specific …
better comprehend long complicated sentences and obtain accurate aspect-specific …
Vision-language pre-training for multimodal aspect-based sentiment analysis
As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis
(MABSA) has attracted increasing attention in recent years. However, previous approaches …
(MABSA) has attracted increasing attention in recent years. However, previous approaches …
Multi-source semantic graph-based multimodal sarcasm explanation generation
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 …
generate a natural language sentence for a multimodal social post (an image as well as its …
Summary-oriented vision modeling for multimodal abstractive summarization
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 …
multimodal data (text and vision). Existing studies mainly focus on how to effectively use the …
Unisa: Unified generative framework for sentiment analysis
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 …
predict emotional categories based on multimodal information. It consists of several …
Recent advances in neural text generation: A task-agnostic survey
In recent years, considerable research has been dedicated to the application of neural
models in the field of natural language generation (NLG). The primary objective is to …
models in the field of natural language generation (NLG). The primary objective is to …
Few-shot adaptation of multi-modal foundation models: A survey
F Liu, T Zhang, W Dai, W Cai, X Zhou… - arXiv preprint arXiv …, 2024 - arxiv.org
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 visual …
pre-training models (eg, ImageNet-based pre-training) as the new generation of visual …