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 …

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 …

Recently, the vision transformer and its variants have played an increasingly important role
in both monocular and multi-view human pose estimation. Considering image patches as …

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 …

Recently, Deepfake has drawn considerable public attention due to security and privacy
concerns in social media digital forensics. As the wildly spreading Deepfake videos on the …

Vision Transformers (ViTs) have recently garnered considerable attention, emerging as a
promising alternative to convolutional neural networks (CNNs) in several vision-related …

Machine and deep learning techniques are two branches of artificial intelligence that have
proven very efficient in solving advanced human problems. The automotive industry is …

Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer,
have recently gained significant traction in computer vision tasks due to their ability to …

Transformer neural networks are rapidly being integrated into state-of-the-art solutions for
natural language processing (NLP) and computer vision. However, the complex structure of …

The computer vision community is embracing two promising learning paradigms: the Vision
Transformer (ViT) and Multi-task Learning (MTL). ViT models show extraordinary …