Results 31 to 40 of about 33,786 (306)
Supervised deep learning with vision transformer predicts delirium using limited lead EEG
Scientific Reports, 2023 As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool.
Malissa A. Mulkey +4 more
doaj +1 more source
Distinguishing Malicious Drones Using Vision Transformer
AI, 2022 Drones are commonly used in numerous applications, such as surveillance, navigation, spraying pesticides in autonomous agricultural systems, various military services, etc., due to their variable sizes and workloads.
Sonain Jamil +2 more
doaj +1 more source
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
, 2021 Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks.
Deng-Ping Fan +17 more
core +1 more source
QuadTree Attention for Vision Transformers
CoRR, 2022 Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc.
Tang, Shitao +3 more
openaire +4 more sources
RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers
Sensors, 2022 The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the ...
Hatem Ibrahem +2 more
doaj +1 more source
3D CATBraTS: Channel Attention Transformer for Brain Tumour Semantic Segmentation [PDF]
, 2023 Brain tumour diagnosis is a challenging task yet crucial for planning treatments to stop or slow the growth of a tumour. In the last decade, there has been a dramatic increase in the use of convolutional neural networks (CNN) for their high performance ...
Bonmati Coll, E. +3 more
core +1 more source
Semi-supervised Vision Transformers
, 2022 We study the training of Vision Transformers for semi-supervised image classification. Transformers have recently demonstrated impressive performance on a multitude of supervised learning tasks. Surprisingly, we show Vision Transformers perform significantly worse than Convolutional Neural Networks when only a small set of labeled data is available ...
Zejia Weng +4 more
openaire +2 more sources
, 2023 Rapid analysis of materials characterization spectra is pivotal for preventing accumulation of unwieldy datasets, thus accelerating subsequent decision-making.
Yunchao, Xie +5 more
core +1 more source
Ε(2)-Equivariant Vision Transformer [PDF]
, 2023 Vision Transformer (ViT) has achieved remarkable performance in computer vision. However, positional encoding in ViT makes it substantially difficult to learn the intrinsic equivariance in data.
Yang, Kaifan +3 more
core +1 more source
Privacy-Preserving Semantic Segmentation Using Vision Transformer
Journal of Imaging, 2022 In this paper, we propose a privacy-preserving semantic segmentation method that uses encrypted images and models with the vision transformer (ViT), called the segmentation transformer (SETR).
Hitoshi Kiya +3 more
doaj +1 more source