Results 61 to 70 of about 7,252 (198)

Vision language models for bridge inspections: A review of applications in image‐based damage documentation

Structural Concrete, EarlyView.
Abstract Automating bridge inspections requires more than detecting individual damage instances. It demands systems capable of describing, contextualizing, and interpreting damage in an inspection‐relevant manner. Conventional computer vision approaches, such as object detection and segmentation, primarily address visual recognition tasks and are ...
Rona Firdes Çelik, Ulas Bagci, Sylvia Kessler   +2 more
wiley   +1 more source

Swin-FER: Swin Transformer for Facial Expression Recognition

Applied Sciences
The ability of transformers to capture global context information is highly beneficial for recognizing subtle differences in facial expressions. However, compared to convolutional neural networks, transformers require the computation of dependencies between each element and all other elements, leading to high computational complexity. Additionally, the
Mei Bie, Huan Xu, Yan Gao, Kai Song, Xiangjiu Che   +4 more
openaire   +2 more sources

Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning

Bioengineering
Deep learning has achieved widespread adoption for medical image diagnosis, with extensive research dedicated to mammographic image analysis for breast cancer screening.
Kenji Yoshitsugu, Kazumasa Kishimoto, Tadamasa Takemura   +2 more
doaj   +1 more source

A Multi‐Sequence Adversarial Fusion U‐Net for Brain Tumor Image Segmentation

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
In the field of brain tumor image segmentation, in order to avoid the impact of insufficient number of training samples, the method of fusing multi‐modal MRI information before segmentation is widely used. However, when fusing different modal features, existing methods only add fixed weights to the features of each modality, resulting in insufficient ...
Jie Wang, Jinglu Hu
wiley   +1 more source

AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam, Basit Raza, Muhammad Faheem, Aadil Raza   +3 more
wiley   +1 more source

Swin-HViT for accurate crop disease prediction using an adaptive hybrid transformer model

Discover Artificial Intelligence
Agriculture plays a pivotal role in global economic growth, yet it faces significant challenges from pests and crop diseases. Early detection is crucial for preventing large-scale crop losses and ensuring food security.
Hemalatha Gunasekaran, N. R. Wilfred Blessing, Naveen Vijayakumar Watson, B. Hariharan, Angelin Gladys Jesudoss, C. G. Anupama   +5 more
doaj   +1 more source

DrLS: Distortion‐Resistant Lossless Steganography via Colour Depth Interpolation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT The lossless data steganography is to hide a certain amount of information into a container image. Previous lossless steganography methods fail to strike a balance between capacity, imperceptibility, accuracy, and robustness, commonly vulnerable to distortion on container images.
Youmin Xu, Xuanyu Zhang, Xiandong Meng, Jian Zhang   +3 more
wiley   +1 more source

A Lightweight Hybrid Network for Medical Image Segmentation With Adaptive Feature Selection

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurate medical image segmentation with low model complexity remains difficult because lesions are often small in scale and boundary cues are easily corrupted by noise. Although recent segmentation methods have achieved strong performance, many of them rely on increasingly complex architectures with high computational costs, limiting their ...
Zhouwei Lin, Yudong Xia, Pingping Wu, Han Lin, Qingsong Xu, Zheng Fan, Juan Tu, Jun Wu   +7 more
wiley   +1 more source

TMSA‐Net: Transformer‐Based Multi‐Scale Attention U‐Net for Flood Image Segmentation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Flood detection is essential for real‐time applications, including disaster management, emergency response, and alerting people in flood zones. For successful flood detection, accurate flood region segmentation is essential. However, the flood region segmentation is challenging due to the complex background and occlusions with debris and the ...
Parham Imanzadeh Charandabi, Shivakumara Palaiahnakote, Apostolos Antonacopoulos, Christian Clausner   +3 more
wiley   +1 more source

Classifying Deepfakes Using Swin Transformers

CoRR
3 ...
Aprille J. Xi, Eason Chen
openaire   +2 more sources