Results 21 to 30 of about 372,115 (318)

A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image Classification

Remote Sensing, 2021
Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance.
Dongxu Liu, Guangliang Han, Peixun Liu, Hang Yang, Xinglong Sun, Qingqing Li, Jiajia Wu   +6 more
doaj   +1 more source

Glass Defect Detection via Multi-Scale Feature Fusion

Journal of Physics: Conference Series, 2022
Abstract Glass defect detection is significant in glass industry. However, most of the existing methods for glass defect detection currently still rely on manual screening with high-cost and poor-efficiency. To address this issue, we propose a glass defect detection method using multi-scale feature fusion strategy. Specifically, we first
Haiying Huang, Qiugang Zhan, Xiurui Xie, Dongsheng Ye, Guisong Liu   +4 more
openaire   +1 more source

Revisiting Multi-Scale Feature Fusion for Semantic Segmentation

, 2022
It is commonly believed that high internal resolution combined with expensive operations (e.g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in slow speed and large memory usage. In this paper, we question this belief and demonstrate that neither high internal resolution nor atrous convolutions are necessary.
Meng, Tianjian, Ghiasi, Golnaz, Mahjourian, Reza, Le, Quoc V., Tan, Mingxing   +4 more
openaire   +2 more sources

MSFN-YOLOv11: A Novel Multi-Scale Feature Fusion Recognition Model Based on Improved YOLOv11 for Real-Time Monitoring of Birds in Wetland Ecosystems. [PDF]

Animals (Basel)
Wang L, Ye L, Chen X, Chu N.
europepmc   +2 more sources

Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation [PDF]

, 2019
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation ...
Aslani, Shahab, Dayan, Michael, Filippi, Massimo, Murino, Vittorio, Rocca, Maria A, Sona, Diego, Storelli, Loredana   +6 more
core   +2 more sources

Attention Fusion of Transformer-Based and Scale-Based Method for Hyperspectral and LiDAR Joint Classification

Remote Sensing, 2023
In recent years, there have been many multimodal works in the field of remote sensing, and most of them have achieved good results in the task of land-cover classification.
Maqun Zhang, Feng Gao, Tiange Zhang, Yanhai Gan, Junyu Dong, Hui Yu   +5 more
doaj   +1 more source

Single-Stage Object Detection Algorithm Based on Dilated Convolution and Feature Enhancement [PDF]

Jisuanji gongcheng, 2021
The shallow feature map of the object detection algorithm based on Convolutional Neural Network(CNN) lacks semantic information,while the deep feature map lacks detailed information.In order to fully exploit shallow and deep feature maps and solve the ...
JIANG Jun, ZHAI Donghai
doaj   +1 more source

Face Super-Resolution via Multi-Scale Feature Fusion

, 2022
Abstract Due to equipment limitations, faces collected by public monitoring platforms often have low resolution. To solve this problem, a face super-resolution model based on deep learning with multi-scale feature fusion is proposed. The low-resolution faces will be processed by this model to obtain faces with high definition, which greatly ...
Yujiao Zhang, Zhenxue Chen, Jiaqian Cao, Luna Sun, Wencheng Wang   +4 more
openaire   +1 more source

MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation

IEEE Access, 2020
Automatic assessing the location and extent of liver and liver tumor is critical for radiologists, diagnosis and the clinical process. In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the ...
Tongle Fan, Guanglei Wang, Yan Li, Hongrui Wang   +3 more
doaj   +1 more source

Multi-patch and Multi-scale Hierarchical Aggregation Network for Fast Nonhomogeneous ImageDehazing [PDF]

Jisuanji kexue, 2021
Despite dehazing algorithms based on convolutional neural networks have made tremendous progress in synthetic uniform hazy datasets,they still perform poorly on real nonhomogeneous hazy images.In order to achieve fast and effective nonhomogeneous image ...
YANG Kun, ZHANG Juan, FANG Zhi-jun
doaj   +1 more source