Enhancing medical image segmentation with a multi-transformer U-Net [PDF]
PeerJVarious segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach
Yongping Dan +3 more
doaj +2 more sources
Estimating glue-layer defects on plywood through computer vision methods [PDF]
, 2023 Making quality film-faced plywood is a vital issue for the manufacturer as it decides the quality of the end product made from this film-faced plywood.
Baad, Swapnil
core
A wheat spike detection method based on Transformer
Frontiers in Plant Science, 2022 Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs ...
Qiong Zhou +11 more
doaj +1 more source
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window
, 2023 Following the success in language domain, the self-attention mechanism (transformer) is adopted in the vision domain and achieving great success recently. Additionally, as another stream, multi-layer perceptron (MLP) is also explored in the vision domain.
Go, Mocho, Tachibana, Hideyuki
core +1 more source
Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography [PDF]
, 2022 Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases.
Alam, Md Golam Rabiul +5 more
core +2 more sources
Center Point Target Detection Algorithm Based on Improved Swin Transformer [PDF]
Jisuanji kexueAiming at the shortcomings of Swin Transformer in extracting local feature information and expressing features,this paper proposes a center point target detection algorithm based on improved Swin Transformer to improve its performance in target detection.
LIU Jiasen, HUANG Jun
doaj +1 more source
SSformer: A Lightweight Transformer for Semantic Segmentation
, 2022 It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of inductive biases of local neighborhoods and possess a high time complexity.
Gao, Pan, Shi, Wentao, Xu, Jing
core
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 +5 more
doaj +1 more source
Swin-transformer for weak feature matching. [PDF]
Sci RepFeature 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 +3 more sources
STUCNET – SWIN TRANSFORMER-V2 UNET FOR CRACK SEGMENTATION NETWORK [PDF]
, 2023 Automatic crack detection on road surfaces is an important task for supporting the quality control of road infrastructure in transportation. Various methods have been proposed for crack segmentation, but their accuracy is still limited.
Nguyen, Le Hoang Tung, Phan, Hai-Hong
core +1 more source