Results 11 to 20 of about 1,457,631 (249)

CSiT: A Multiscale Vision Transformer for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
The hyperspectral image (HSI) has nearly continuous spectral information; thus, the target of interest can be accurately identified by the subtle details of spectral properties.
Wenxuan He, Weiliang Huang, Shuhong Liao, Zhen Xu, Jingwen Yan   +4 more
doaj   +1 more source

Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images

Sensors, 2023
Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images.
Jia Song, A-Xing Zhu, Yunqiang Zhu
doaj   +1 more source

Dual Vision Transformer

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Prior works have proposed several strategies to reduce the computational cost of self-attention mechanism. Many of these works consider decomposing the self-attention procedure into regional and local feature extraction procedures that each incurs a much smaller computational complexity.
Ting Yao 0003, Yehao Li, Yingwei Pan, Yu Wang 0060, Xiao-Ping Zhang 0002, Tao Mei 0001   +5 more
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Scaling Vision Transformers

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, Alexander Kolesnikov 0003, Neil Houlsby, Lucas Beyer   +3 more
openaire   +4 more sources

A Review of Transformer-Based Approaches for Image Captioning

Applied Sciences, 2023
Visual understanding is a research area that bridges the gap between computer vision and natural language processing. Image captioning is a visual understanding task in which natural language descriptions of images are automatically generated using ...
Oscar Ondeng, Heywood Ouma, Peter Akuon
doaj   +1 more source

STHarDNet: Swin Transformer with HarDNet for MRI Segmentation

Applied Sciences, 2022
In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder–decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder–decoder model ...
Yeonghyeon Gu, Zhegao Piao, Seong Joon Yoo   +2 more
doaj   +1 more source

LAVT: Language-Aware Vision Transformer for referring image segmentation [PDF]

, 2022
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Zhao, H, Yang, Z, Yang, Zhao, Torr, Philip H.S., Tang, Y, Torr, P, Wang, J, Chen, K, Chen, Kai, Torr, Philip, Zhao, Hengshuang, Wang, Jiaqi, Tang, Yansong   +12 more
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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 0007, Philip H. S. Torr, Wayne Zhang 0001, Dahua Lin   +6 more
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Diverse features discovery transformer for pedestrian attribute recognition [PDF]

, 2023
Recently, Swin Transformer has been widely explored as a general backbone for computer vision, which helps to improve the performance of vision tasks due to the ability to establish associations for long-range dependencies of different spatial locations.
Hussain, Amir, Zheng, Aihua, Wang, Huimin, He, Ran, Wang, Jiaxiang, Huang, Huaibo   +5 more
core   +1 more source

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