Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often
lets those tasks learn better jointly. Multi-tasking models have become successful and often …

Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

In recent years, attention-based models have achieved impressive performance in natural
language processing and computer vision applications by effectively capturing contextual …

Although Transformer-based language representations achieve state-of-the-art accuracy on
various natural language processing (NLP) tasks, the large model size has been …

The Transformer has been an indispensable staple in deep learning. However, for real-life
applications, it is very challenging to deploy efficient Transformers due to the immense …

Large language models (LLMs) have emerged as powerful tools for natural language
processing tasks, revolutionizing the field with their ability to understand and generate …

Vision transformers (ViTs) are emerging with significantly improved accuracy in computer
vision tasks. However, their complex architecture and enormous computation/storage …