Results 21 to 30 of about 1,582,815 (295)
ViTA: A Vision Transformer Inference Accelerator for Edge Applications
, 2023 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 capture the global relation between features which leads to superior ...
Beerel, Peter A. +4 more
core +1 more source
Reversible Vision Transformers
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reversible Vision Transformers enable scaling up architectures with efficient memory usage.
Karttikeya Mangalam +6 more
openaire +3 more sources
Transformer architectures for computer vision: A comprehensive review and future research directions [PDF]
EPJ Web of ConferencesLong-range dependencies and contextual relationships in videos were captured by using Convolutional Neural Networks (CNNs) in past. Recently the use of Transformers is started for capturing the long-range dependencies and contextual relationships in ...
Ugile Tukaram, Uke Nilesh
doaj +1 more source
Handwritten Text Recognition Using a Vision Transformer
, 2023 The aim of this project is to create a method for offline handwritten text recognition using a vision transformer. It consists of two parts, where the first one segments all words in a document into separate images and the second one which recognizes the
Kurén, Jonathan, Sundberg, Martin
core +2 more sources
Transformer-based ripeness segmentation for tomatoes
Smart Agricultural Technology, 2023 With the recent development of computer vision technology, various computer vision techniques have been applied to agriculture. Recently, the Transformer network has been introduced to image recognition, which allows a different approach to extracting ...
Risa Shinoda +3 more
doaj +1 more source
Vision Transformers Are Robust Learners
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is their robustness evaluation and attribution.
Sayak Paul, Pin-Yu Chen
openaire +3 more sources
Art authentication with vision transformers
Neural Computing and Applications, 2023 AbstractIn recent years, transformers, initially developed for language, have been successfully applied to visual tasks. Vision transformers have been shown to push the state of the art in a wide range of tasks, including image classification, object detection, and semantic segmentation.
Schaerf, Ludovica +2 more
openaire +4 more sources
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
Vision Transformer in Industrial Visual Inspection
Applied Sciences, 2022 Artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a potential paradigm shift and have the potential to facilitate an ...
Nils Hütten +2 more
doaj +1 more source
Splitting expands the application range of Vision Transformer -- variable Vision Transformer (vViT) [PDF]
, 2023 Vision Transformer (ViT) has achieved outstanding results in computer vision. Although there are many Transformer-based architectures derived from the original ViT, the dimension of patches are often the same with each other. This disadvantage leads to a
Usuzaki, Takuma
core