80 citations · 311 across the 38 of their papers we have counts for
7 papers · 1 filter
Modeling Multi-modal Cross-interaction for Multi-label Few-shot Image Classification Based on Local Feature Selection
Kun Yan, Zied Bouraoui, Fangyun Wei +4
The aim of multi-label few-shot image classification (ML-FSIC) is to assign semantic labels to images, in settings where only a small number of training examples are available for…
GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
Lingzhi Shen, Yunfei Long, Xiaohao Cai +4
Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. Existing detection methods typically rely on fusion effectiveness a…
NarrationDep: Narratives on Social Media For Automatic Depression Detection
Hamad Zogan, Imran Razzak, Shoaib Jameel +1
Social media posts provide valuable insight into the narrative of users and their intentions, including providing an opportunity to automatically model whether a social media user…
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Ubaid Azam, Imran Razzak, Shelly Vishwakarma +3
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications. While transformer-based deep neural networks (DNNs) like BERT have demonstra…
From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions
Ubaid Azam, Imran Razzak, Shelly Vishwakarma +3
AI-driven medical predictions with trustworthy confidence are essential for ensuring the responsible use of AI in healthcare applications. The growing capabilities of AI raise ques…
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
Guangming Huang, Yingya Li, Shoaib Jameel +2
Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent…
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification
Abdullah Alsuhaibani, Hamad Zogan, Imran Razzak +2
Language models such as Bidirectional Encoder Representations from Transformers (BERT) have been very effective in various Natural Language Processing (NLP) and text mining tasks i…