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Rosette Trajectory MRI Reconstruction with Vision Transformers [PDF]

Tomography
Introduction: An efficient pipeline for rosette trajectory magnetic resonance imaging reconstruction is proposed, combining the inverse Fourier transform with a vision transformer (ViT) network enhanced with a convolutional layer.
Muhammed Fikret Yalcinbas, Cengizhan Ozturk, Onur Ozyurt, Uzay E. Emir, Ulas Bagci   +4 more
doaj   +2 more sources

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Applied Sciences, 2021
Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios.
Muhammad Tahir, Saeed Anwar
doaj   +1 more source

Self-attention in vision transformers performs perceptual grouping, not attention

Frontiers in Computer Science, 2023
Recently, a considerable number of studies in computer vision involve deep neural architectures called vision transformers. Visual processing in these models incorporates computational models that are claimed to implement attention mechanisms. Despite an
Paria Mehrani, John K. Tsotsos
doaj   +1 more source

ViTFER: Facial Emotion Recognition with Vision Transformers

Applied System Innovation, 2022
In several fields nowadays, automated emotion recognition has been shown to be a highly powerful tool. Mapping different facial expressions to their respective emotional states is the main objective of facial emotion recognition (FER).
Aayushi Chaudhari, Chintan Bhatt, Achyut Krishna, Pier Luigi Mazzeo   +3 more
doaj   +1 more source

Multi-Manifold Attention for Vision Transformers

IEEE Access, 2023
Vision Transformers are very popular nowadays due to their state-of-the-art performance in several computer vision tasks, such as image classification and action recognition. Although their performance has been greatly enhanced through highly descriptive
Dimitrios Konstantinidis, Ilias Papastratis, Kosmas Dimitropoulos, Petros Daras   +3 more
doaj   +1 more source

Transformers in Remote Sensing: A Survey

Remote Sensing, 2023
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded ...
Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz Khan   +6 more
doaj   +1 more source

Large-Scale Date Palm Tree Segmentation from Multiscale UAV-Based and Aerial Images Using Deep Vision Transformers

Drones, 2023
The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management.
Mohamed Barakat A. Gibril, Helmi Zulhaidi Mohd Shafri, Rami Al-Ruzouq, Abdallah Shanableh, Faten Nahas, Saeed Al Mansoori   +5 more
doaj   +1 more source

Wildfire Segmentation Using Deep Vision Transformers

Remote Sensing, 2021
In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving ...
Rafik Ghali, Moulay A. Akhloufi, Marwa Jmal, Wided Souidene Mseddi, Rabah Attia   +4 more
doaj   +1 more source

A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles

IEEE Open Journal of Intelligent Transportation Systems, 2023
In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation ...
Durga Prasad Bavirisetti, Herman Ryen Martinsen, Gabriel Hanssen Kiss, Frank Lindseth   +3 more
doaj   +1 more source

Vision Transformers for Vein Biometric Recognition

IEEE Access, 2023
In October 2020, Google researchers present a promising Deep Learning architecture paradigm for Computer Vision that outperforms the already standard Convolutional Neural Networks (CNNs) on multiple image recognition state-of-the-art datasets: Vision ...
Raul Garcia-Martin, Raul Sanchez-Reillo
doaj   +1 more source