Results 21 to 30 of about 1,373,967 (287)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively.
Xiaohua Zhai +3 more
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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 +5 more
doaj +1 more source
Vision Transformer with Progressive Sampling [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Accepted to ICCV ...
Xiaoyu Yue +6 more
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Building Extraction With Vision Transformer [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Submitted to ...
Libo Wang +3 more
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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 +4 more
doaj +1 more source
International Journal of Computer Vision, 2023 We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only one ViT model at a time, but is capable of providing improved image recognition performance with various computational costs. Here, the trained ViT model, termed super vision
Mingbao Lin +6 more
openaire +3 more sources
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
Self-supervised Vision Transformers for 3D Pose Estimation of Novel Objects [PDF]
, 2023 Object pose estimation is important for object manipulation and scene understanding. In order to improve the general applicability of pose estimators, recent research focuses on providing estimates for novel objects, that is, objects unseen during ...
Thalhammer, Stefan +3 more
core +2 more sources
Self-Supervised Vision Transformers for Malware Detection
IEEE Access, 2022 Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming ...
Sachith Seneviratne +3 more
doaj +1 more source
UHF diagnostic monitoring techniques for power transformers [PDF]
, 2004 This paper initially gives an introduction to ultra-high frequency (UHF) partial discharge monitoring techniques and their application to gas insulated substations.
Bennoch, C.J. +3 more
core +3 more sources