Results 31 to 40 of about 5,326,339 (296)
FULLY CONVOLUTIONAL NETWORK BASED SHADOW EXTRACTION FROM GF-2 IMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building ...
Z. Li +5 more
doaj +1 more source
Fully Convolutional Neural Network Structure and Its Loss Function for Image Classification
IEEE Access, 2022 The overall structure of a convolutional neural network classifier includes multiple convolutional layers and one or more linear layers. Due to the fully connected characteristics of linear layer networks, there are usually many parameters, which may ...
Qiuyu Zhu, Xuewen Zu
doaj +1 more source
Lidar Cloud Detection With Fully Convolutional Networks [PDF]
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 Updated for full version of paper. 10 pages, submitted to NIPS 2018 Conference (in review)
Erol Cromwell, Donna Flynn
openaire +2 more sources
Convolutional Ensemble Network for Image Classification [PDF]
, 2022 Convolutional Neural Networks (CNNs) have been widely acclaimed for image classification tasks in the last decade. Despite the recent success, training a CNN for large datasets from scratch is still a challenging task due to the long training time and ...
Sinha, Toshi, Verma, Brijesh
core +1 more source
Fully Hyperbolic Graph Convolution Network for Recommendation [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective
Liping Wang +3 more
openaire +3 more sources
Tire Defect Detection Using Fully Convolutional Network
IEEE Access, 2019 A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN ...
Ren Wang +3 more
doaj +1 more source
Fully Symmetric Convolutional Network for Effective Image Denoising
Applied Sciences, 2019 Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional⁻deconvolutional neural network (FSCN) is proposed for image denoising.
Steffi Agino Priyanka, Yuan-Kai Wang
doaj +1 more source
sagieppel/Fully-convolutional-neural-network-FCN-for-semantic-segmentation-with-pytorch: 1.0
, 2021 <p>1</p ...
sagieppel
core +1 more source
Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation
Applied Sciences, 2020 The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin.
Karshiev Sanjar +5 more
doaj +1 more source