Results 101 to 110 of about 18,617 (276)

Two-Stage Underwater Object Detection Network Using Swin Transformer [PDF]

, 2022
Jia Liu, Shuang Liu, Shujuan Xu, Changjun Zhou   +3 more
openalex   +1 more source

Improving Medium‐Range Temperature Forecast Over the Tibetan Plateau Through Spatially Adaptive Fusion

Geophysical Research Letters, Volume 53, Issue 10, 28 May 2026.
Abstract Tibetan Plateau exerts profound impacts on the global weather system, whereas its medium‐range temperature forecast is challenging due to the complex topography. This study introduces Swin Transformer Fusion (STF), a spatially adaptive ensemble strategy that integrates forecasts from numerical weather prediction models (EC, GFS) and large ...
Yanfeng Wang, Bowen Zhao, Ping Huang, Lin Wang, Haosu Tang, Shuhao Ge, Mingwei Lu, Jingling Tang, Yupeng Teng   +8 more
wiley   +1 more source

Multi-scale capsule Swin Transformer-based method for SAR image target recognition

Tongxin xuebao
A multi-scale capsule Swin Transformer network (MSCSTN) was proposed by synergizing the semantic feature encoding of capsule units with the context feature mapping of Swin Transformer. Capsule encoding and the Swin Transformer were jointly applied to SAR
HOU Yuchao, WANG Jie, LI Hongtao, HAO Yan, DUAN Xiaoqi, HUANG Kaiwen, TIAN Youliang   +6 more
doaj  

Retinal disease classification from OCT images using EfficientNetB3 with neutrosophic similarity score enhancement

Journal of Applied Clinical Medical Physics, Volume 27, Issue 5, May 2026.
Abstract Purpose Automated classification of retinal diseases from optical coherence tomography (OCT) images plays a significant role in supporting early diagnosis and clinical decision‐making. In this study, a deep learning (DL) framework is developed by integrating a neutrosophic similarity score (NSS) with the EfficientNetB3 to improve image ...
Devi M, G. Hannah Grace
wiley   +1 more source

Off-line identifying Script Writers by Swin Transformers and ResNeSt-50

ELCVIA Electronic Letters on Computer Vision and Image Analysis
In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50.
Afef Kacem Echi, Takwa Ben Aïcha Gader
doaj   +1 more source

Automated Glaucoma Detection Using Vision and Swin Transformers: Advancing Ophthalmic AI [PDF]

Purpose:Glaucoma is one of the most common causes of permanent blindness in the world; early detection and precise diagnosis are essential to successful treatment.Convolutional Neural Networks (CNNs) are one of the deep learning techniques that have ...
Gireesh, Dr.N., Sakunthala, D.
core   +2 more sources

SpineMAE: A bone‐window self‐supervised and structure‐aware framework for 3D cervical vertebra segmentation and fracture classification

Journal of Applied Clinical Medical Physics, Volume 27, Issue 5, May 2026.
Abstract Introduction Accurate segmentation and classification of cervical spine fractures are essential for timely diagnosis and clinical decision‐making in trauma care. Existing deep learning approaches often require extensive manual annotations and struggle to maintain anatomical consistency across vertebral levels, limiting their reliability and ...
Qing Liang, Jingding Zhao, Fang Yang, Lin Shi, Yang Song, Xianjun Chen, Zewen Shi, Qingjiang Pang   +7 more
wiley   +1 more source

Pavement Crack Detection Based on the Improved Swin-Unet Model

Buildings
Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip ...
Song Chen, Zhixuan Feng, Guangqing Xiao, Xilong Chen, Chuxiang Gao, Mingming Zhao, Huayang Yu   +6 more
doaj   +1 more source

Semantic Segmentation of Remote Sensing Images Based on Swin Transformer [PDF]

, 2023
Yinghao Lin, Shihao Zhao, Yuye Wang, Yi Xie, Baojun Qiao   +4 more
openalex   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, Volume 8, Issue 5, May 2026.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source