Results 11 to 20 of about 7,104 (161)

Swin-HSSAM: A green coffee bean grading method by Swin transformer. [PDF]

PLoS ONE
A 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, Yuqing Zhao, Aoying Jia, Tianyun Wang, Jiashun Li, Kaiming Xiang, Hangyu Deng, Maochang He, Rui Jiang, Yue Zhang   +9 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, Zhegao Piao, Seong Joon Yoo   +2 more
doaj   +3 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, Zongnan Zhu, Weishou Jin, Zhuo Li   +3 more
doaj   +4 more sources

Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification

Frontiers in Oncology
Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes.
Manal Abdullah Alohali, Nora El-Rashidy, Saad Alaklabi, Hela Elmannai, Saleh Alharbi, Hager Saleh, Hager Saleh, Hager Saleh   +7 more
doaj   +3 more sources

Quantum integration in swin transformer mitigates overfitting in breast cancer screening [PDF]

Scientific Reports
To explore the potential of quantum computing in advancing transformer-based deep learning models for breast cancer screening, this study introduces the Quantum-Enhanced Swin Transformer (QEST).
Zongyu Xie, Xiaoguang Yang, Shuni Zhang, Jingru Yang, Yun Zhu, Aoqi Zhang, Haitao Sun, Qun Dai, Lei Li, Hongde Liu, Wenlong Ming, Menghan Dou   +11 more
doaj   +2 more sources

A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction

Remote Sensing, 2022
Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks.
Xiao Xiao, Wenliang Guo, Rui Chen, Yilong Hui, Jianing Wang, Hongyu Zhao   +5 more
doaj   +3 more sources

Swin-transformer for weak feature matching. [PDF]

Sci Rep
Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes.
Guo Y, Li W, Zhai P.
europepmc   +4 more sources

SPT-Swin: A Shifted Patch Tokenization Swin Transformer for Image Classification

IEEE Access
Recently, the transformer-based model e.g., the vision transformer (ViT) has been extensively used in computer vision tasks. The superior performance of the ViT leads to the requirement of an enormous dataset and the complexity of calculating self ...
Gazi Jannatul Ferdous, Khaleda Akhter Sathi, Md. Azad Hossain, M. Ali Akber Dewan   +3 more
doaj   +2 more sources

Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs

Diagnostics, 2023
The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs.
Xiaojie Zhou, Guoxia Yu, Qiyue Yin, Jun Yang, Jiangyang Sun, Shengyi Lv, Qing Shi   +6 more
doaj   +1 more source

Video Swin Transformer

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
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, Jia Ning, Yue Cao 0001, Yixuan Wei, Zheng Zhang 0022, Stephen Lin 0001, Han Hu 0001   +6 more
openaire   +2 more sources