Results 71 to 80 of about 1,683,190 (291)
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
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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
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Survey of Vision Transformers(ViT) [PDF]
Jisuanji kexueThe 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
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Measurements With A Quantum Vision Transformer: A Naive Approach [PDF]
EPJ Web of ConferencesIn 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 +2 more
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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 +5 more
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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
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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
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, 2023 The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive amounts of data using significant computation power.
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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 +3 more
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Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification
Sensors, 2022 Ice environments pose challenges for conventional underwater acoustic localization techniques due to their multipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks ...
Steven Whitaker +3 more
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