Results 11 to 20 of about 2,105 (150)
STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
Applied Sciences (Switzerland), 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 ...
Zhegao Piao, Seong Joon Yoo
exaly +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 +3 more
doaj +5 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.
Yilong Hui +2 more
exaly +3 more sources
SPT-Swin: A Shifted Patch Tokenization Swin Transformer for Image Classification
IEEE AccessRecently, 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 +3 more
doaj +2 more sources
Swin-HSSAM: A green coffee bean grading method by Swin transformer.
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
doaj +3 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 +6 more
doaj +1 more source
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 +6 more
openaire +3 more sources
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 patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiological
Jiahao Huang +8 more
openaire +5 more sources
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 inefficient algorithm.
Krisna Pinasthika +4 more
openaire +4 more sources
Transformer-based ripeness segmentation for tomatoes
Smart Agricultural Technology, 2023 With the recent development of computer vision technology, various computer vision techniques have been applied to agriculture. Recently, the Transformer network has been introduced to image recognition, which allows a different approach to extracting ...
Risa Shinoda +3 more
doaj +1 more source