Results 21 to 30 of about 96,008 (322)

Building Extraction With Vision Transformer [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2022
Submitted to ...
Libo Wang, Shenghui Fang, Xiaoliang Meng, Rui Li 0036   +3 more
openaire   +2 more sources

Large-Scale Date Palm Tree Segmentation from Multiscale UAV-Based and Aerial Images Using Deep Vision Transformers

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, Helmi Zulhaidi Mohd Shafri, Rami Al-Ruzouq, Abdallah Shanableh, Faten Nahas, Saeed Al Mansoori   +5 more
doaj   +1 more source

Peripheral Vision Transformer

Advances in Neural Information Processing Systems 35, 2022
Human vision possesses a special type of visual processing systems called peripheral vision. Partitioning the entire visual field into multiple contour regions based on the distance to the center of our gaze, the peripheral vision provides us the ability to perceive various visual features at different regions.
Juhong Min, Yucheng Zhao, Chong Luo 0001, Minsu Cho   +3 more
openaire   +3 more sources

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, Moulay A. Akhloufi, Marwa Jmal, Wided Souidene Mseddi, Rabah Attia   +4 more
doaj   +1 more source

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

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, Haoqi Fan 0001, Yanghao Li, Chao-Yuan Wu, Bo Xiong, Christoph Feichtenhofer, Jitendra Malik   +6 more
openaire   +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, Ridwan Shariffdeen, Sanka Rasnayaka, Nuran Kasthuriarachchi   +3 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, Shuyang Sun, Zhanghui Kuang, Meng Wei, Philip H. S. Torr, Wayne Zhang 0001, Dahua Lin   +6 more
openaire   +3 more sources

A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles

IEEE Open Journal of Intelligent Transportation Systems, 2023
In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation ...
Durga Prasad Bavirisetti, Herman Ryen Martinsen, Gabriel Hanssen Kiss, Frank Lindseth   +3 more
doaj   +1 more source

Transformer architectures for computer vision: A comprehensive review and future research directions [PDF]

EPJ Web of Conferences
Long-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