Dot-product attention has wide applications in computer vision and natural language
processing. However, its memory and computational costs grow quadratically with the input …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

Object detection as a subfield within computer vision has achieved remarkable progress,
which aims to accurately identify and locate a specific object from images or videos. Such …

Fully-supervised deep learning segmentation models are inflexible when encountering new
unseen semantic classes and their fine-tuning often requires significant amounts of …

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 …

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 …