Results 101 to 110 of about 8,226 (198)

Swin Routiformer: Moss Classification Algorithm Based on Swin Transformer With Bi-Level Routing Attention

IEEE Access
Accurate classification of moss species is essential for progress in ecology and biology. However, traditional methods for classifying moss require significant expertise, and current deep learning techniques struggle due to limited dataset diversity and ...
Peichen Li, Huiqin Wang, Zhan Wang, Ke Wang, Chong Wang   +4 more
doaj   +1 more source

Benchmarking Auto‐Regressive Spatio‐Temporal Predictions of Wildfire Spread From the FireBench Simulation Data Set With a Machine Learning Competition

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract As wildfire severity increases, predicting wildfire spread is important for fire and landscape management. However, wildfire spread is a complex phenomenon that is challenging to capture because it depends on many factors, such as wind speed, slope, landscape topography, and fuel properties. Machine Learning (ML) models are powerful tools that
J. Z. Ho, A. Bazgir, A. Asaithambi, J. Hossain, R. Pawłowski, Z. Li, T. Dubail, Q. Wang, C. Gazen, B. Akoush, W. T. Chung, W. Reade, M. Ihme   +12 more
wiley   +1 more source

SE‐Swin: An improved Swin‐Transfomer network of self‐ensemble feature extraction framework for image retrieval

IET Image Processing
The Swin‐Transformer is a variant of the Vision Transformer, which constructs a hierarchical Transformer that computes representations with shifted windows and window multi‐head self‐attention.
Yixuan Xu, Xianbing Wang, Hua Zhang, Hai Lin   +3 more
doaj   +1 more source

Transformers meet CNNs for insights into breast mass classification from histopathological images

Frontiers in Artificial Intelligence
IntroductionBreast cancer remains one of the leading causes of cancer-related deaths among women worldwide, highlighting the critical need for accurate histopathological diagnosis and reliable decision-support systems to improve diagnostic sensitivity ...
Vatsala Anand, Ajay Khajuria
doaj   +1 more source

Transformer face recognition method based on multi-level feature fusion

四川大学学报. 自然科学版
The convolutional operation in a convolutional neural network only captures local information, whereas the Transformer retains more spatial information and can create long-range connections of images. In the application of vision field, Transformer lacks
XIA Gui-Shu, ZHU Zi-Han, WEI Yong-Chao, ZHU Hong-Chao, XU Wei-Qi   +4 more
doaj  

Swin Transformer With Spatial and Local Context Augmentation for Enhanced Semantic Segmentation of Remote Sensing Images

IEEE Open Journal of Signal Processing
Semantic segmentation of remote sensing images is extensively used in crop cover and type analysis, and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the ...
Rong-Xing Ding, Yi-Han Xu, Gang Yu, Wen Zhou, Ding Zhou   +4 more
doaj   +1 more source

Efficient Wheat Disease Identification Using Hybrid Swin-SHARP Vision Model

IEEE Access
Accurate identification of wheat diseases is an essential component for increasing crop yields and guaranteeing global food security. However, subjective opinions, errors, and laborious procedures frequently limit traditional approaches, which are based ...
Waqar Khalid, Yazeed Alkhrijah, Syed Muhammad Usman, Shehzad Khalid   +3 more
doaj   +1 more source

Architectural Scalability and Attention Stabilization for Robust Impulsive Acoustic Spectrogram Classification Using Swin Transformer V2

IEEE Access
Impulsive acoustic event classification based on spectrogram representations remains challenging due to the non-stationary and broadband characteristics of transient signals.
Pafan Doungpaisan, Peerapol Khunarsa
doaj   +1 more source

Capsule-enhanced hierarchical vision transformers for rare disease classification from medical images

Scientific Reports
Automated medical image analysis plays a vital role in rare disease detection, yet existing deep learning models often struggle with severe class imbalance, limited labeled data, and subtle morphological variations.
E. S. Phalguna Krishna, Gowtham Mamidisetti, Sai Srinivas Vellela, Kranthi Kumar Lella, Veeraiah Duggineni, N. Balakrishna   +5 more
doaj   +1 more source

A novel attention-enhanced Swin Transformer for tire surface defect classification

Discover Computing
Tire surface defects are critical factors affecting driving safety, and traditional manual inspection methods suffer from low efficiency, strong subjectivity, and high missed detection rates.
Shanjiang Zhang, Jian Lian, Renjing Liu
doaj   +1 more source