Results 41 to 50 of about 96,008 (322)

3D-Vision-Transformer Stacking Ensemble for Assessing Prostate Cancer Aggressiveness from T2w Images

Bioengineering, 2023
Vision transformers represent the cutting-edge topic in computer vision and are usually employed on two-dimensional data following a transfer learning approach.
Eva Pachetti, Sara Colantonio
doaj   +1 more source

Survey of Vision Transformers(ViT) [PDF]

Jisuanji kexue
The Vision Transformer(ViT),an application of the Transformer architecture with an encoder-decoder structure,has garnered remarkable success in the field of computer vision.Over the past few years,research centered around ViT has witnessed a prolific ...
LI Yujie, MA Zihang, WANG Yifu, WANG Xinghe, TAN Benying
doaj   +1 more source

LIFT: Learned Invariant Feature Transform [PDF]

, 2016
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description.
Fua, Pascal, Lepetit, Vincent, Trulls, Eduard, Yi, Kwang Moo   +3 more
core   +2 more sources

Measurements With A Quantum Vision Transformer: A Naive Approach [PDF]

EPJ Web of Conferences
In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications.
Pasquali Dominic, Grossi Michele, Vallecorsa Sofia   +2 more
doaj   +1 more source

Vision Transformers in Image Restoration: A Survey

Sensors, 2023
The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks.
Anas M. Ali, Bilel Benjdira, Anis Koubaa, Walid El-Shafai, Zahid Khan, Wadii Boulila   +5 more
doaj   +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, Xitong Yang, Ang Li 0001, Zuxuan Wu, Yu-Gang Jiang 0001   +4 more
openaire   +2 more sources

Variable-Rate Deep Image Compression With Vision Transformers

IEEE Access, 2022
Recently, vision transformers have been applied in many computer vision problems due to its long-range learning ability. However, it has not been throughly explored in image compression.
Binglin Li, Jie Liang, Jingning Han
doaj   +1 more source

EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification

Sensors, 2023
Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the models’ high ...
Kian Ming Lim, Chin Poo Lee, Zhi Yang Lee, Ali Alqahtani   +3 more
doaj   +1 more source

Language Modeling with Deep Transformers

, 2019
We explore deep autoregressive Transformer models in language modeling for speech recognition. We focus on two aspects. First, we revisit Transformer model configurations specifically for language modeling. We show that well configured Transformer models
Irie, Kazuki, Ney, Hermann, Schlüter, Ralf, Zeyer, Albert   +3 more
core   +1 more source

Sign language recognition with transformer networks [PDF]

, 2020
Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid ...
Dambre, Joni, De Coster, Mathieu, Van Herreweghe, Mieke   +2 more
core