Results 1 to 10 of about 18,586 (245)
HEAL-SWIN: A Vision Transformer On The Sphere [PDF]
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to ...
Carlsson, Oscar +6 more
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SparseSwin: Swin Transformer with Sparse Transformer Block
Neurocomputing, 2023 Advancements in computer vision research have put transformer architecture as the state of the art in computer vision tasks. One of the known drawbacks of the transformer architecture is the high number of parameters, this can lead to a more complex and ...
Irsal, Riyandi Banovbi Putera +4 more
core +3 more sources
Swin-HSSAM: A green coffee bean grading method by Swin transformer. [PDF]
PLoS ONEA novel shifted window (Swin) Transformer coffee bean grading model called Swin-HSSAM has been proposed to address the challenges of accurately classifying green coffee beans and low identification accuracy.
Yujie Jiao +9 more
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Swin transformer for fast MRI [PDF]
Neurocomputing, 2022 Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of ...
Fang, Yingying +8 more
core +10 more sources
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions.
Ze Liu +6 more
openalex +3 more sources
MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer [PDF]
2024 IEEE International Symposium on Biomedical Imaging (ISBI)Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammography exam, leveraging the inherent correlations in these views to effectively detect tumors. Acknowledging the significance of multi-view
Sushmita Sarker +3 more
openalex +4 more sources
S-Swin Transformer: simplified Swin Transformer model for offline handwritten Chinese character recognition [PDF]
PeerJ Computer Science, 2022 The Transformer shows good prospects in computer vision. However, the Swin Transformer model has the disadvantage of a large number of parameters and high computational effort.
Yongping Dan +3 more
doaj +4 more sources
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
doaj +3 more sources
Asymmetric convolution Swin transformer for medical image super-resolution
Alexandria Engineering Journal, 2023 Medical Image Super-Resolution plays a pivotal role in enhancing diagnostic accuracy. Transformer-based methods, such as Image Restoration Using Swin Transformer (SwinIR) and Swin transformer for fast Magnetic Resonance Imaging (SwinMR), have shown ...
Weijia Lu +9 more
doaj +3 more sources
SwiFT: Swin 4D fMRI Transformer
Advances in Neural Information Processing Systems 36, 2023 Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature
Bae, Sangyoon +8 more
core +3 more sources