Results 11 to 20 of about 1,512,599 (152)
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 +4 more
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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 +5 more
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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
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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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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
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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 +2 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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Sensors, 2023 Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades.
Oumaima Moutik +6 more
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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
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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
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