The multimodal task of Visual Question Answering (VQA) encompassing elements of
Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

We analyze state-of-the-art deep learning models for three tasks: question answering on (1)
images,(2) tables, and (3) passages of text. Using the notion of\emph {attribution}(word …

In this paper, we discuss the initial attempts at boosting understanding human language
based on deep-learning models with quantum computing. We successfully train a quantum …

Large language models (LLMs) often produce unsupported or unverifiable content, known
as" hallucinations." To mitigate this, retrieval-augmented LLMs incorporate citations …

The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with
challenges in delivering timely and accurate information to clients, particularly concerning …

Automatic machine learning-based (ML-based) medical report generation systems for retinal
images suffer from a relative lack of interpretability. Hence, such ML-based systems are still …

Evaluating the quality of automatically generated image descriptions is challenging,
requiring metrics that capture various aspects such as grammaticality, coverage …

When video collections become huge, how to explore both within and across videos
efficiently is challenging. Video summarization is one of the ways to tackle this issue …

In this work, we propose an AI-based method that intends to improve the conventional retinal
disease treatment procedure and help ophthalmologists increase diagnosis efficiency and …