As a special type of transformer, vision transformers (ViTs) can be used for various computer
vision (CV) applications. Convolutional neural networks (CNNs) have several potential …

Recently, vision transformers (ViTs) have been investigated in fine-grained visual
recognition (FGVC) and are now considered state of the art. However, most ViT-based works …

Underwater target detection is a crucial aspect of ocean exploration. However, conventional
underwater target detection methods face several challenges such as inaccurate feature …

Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision
tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting their …

Abstract Vision Transformers (ViTs) have demonstrated powerful representation ability in
various visual tasks thanks to their intrinsic data-hungry nature. However, we unexpectedly …

Abstract Vision Transformers (ViTs) have shown impressive performance but still require a
high computation cost as compared to convolutional neural networks (CNNs), one reason is …

This book has been drafted based on my lectures and seminars from recent years for
postgraduate students at Auckland University of Technology (AUT), New Zealand. We have …

Transformers have demonstrated remarkable accomplishments in several natural language
processing (NLP) tasks as well as image processing tasks. Herein, we present a deep …

Solving image and video jigsaw puzzles poses the challenging task of rearranging image
fragments or video frames from unordered sequences to restore meaningful images and …

The actual collection of tabular data for sharing involves confidentiality and privacy
constraints, leaving the potential risks of machine learning for interventional data analysis …