Hyperspectral and multispectral image fusion via residual selective kernel attention-based U-net
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2024.2318766
Abstract: ABSTRACT The fusion of low-resolution hyperspectral image (LR-HSI) and high-resolution multispectral image (HR-MSI) is a crucial technology for producing high-resolution hyperspectral images. Most existing image fusion algorithms based on deep learning do not fully utilize… read more here.
Keywords: residual selective; image; fusion; attention ... See more keywords
SKFlow: Optical Flow Estimation Using Selective Kernel Networks
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2930293
Abstract: Leveraging on the recent developments in convolutional neural networks (CNNs), optical flow estimation from adjacent frames has been cast as a learning problem, with performance exceeding traditional approaches. The existing networks always use standard convolutional… read more here.
Keywords: level; optical flow; selective kernel; flow estimation ... See more keywords
Deep Neural Networks for Sensor-Based Human Activity Recognition Using Selective Kernel Convolution
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2021.3102735
Abstract: Recently, the state-of-the-art performance in various sensor-based human activity recognition (HAR) tasks has been acquired by deep learning, which can extract automatically features from raw data. In standard convolutional neural networks (CNNs), there is usually… read more here.
Keywords: human activity; selective kernel; based human; har ... See more keywords
Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on Deep Residual Shrinkage Network with Dynamic Convolution and Selective Kernel Attention Model
Sign Up to like & getrecommendations! Published in 2025 at "Algorithms"
DOI: 10.3390/a18090569
Abstract: Rolling bearing vibration signals are often severely affected by strong external noise, which can obscure fault-related features and hinder accurate diagnosis. To address this challenge, this paper proposes an enhanced Deep Residual Shrinkage Network with… read more here.
Keywords: dynamic convolution; kernel attention; network; selective kernel ... See more keywords