A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
2019
Remote Sensing
Preserved Fulltext
This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. ...
Second, the advantages and disadvantages of existing deeplearningbased spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference ...
Figure 1 . 1 Comparison of general architecture between the deep convolutional spatiotemporal fusion network (DCSTFN) and enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) model for Moderate ...
doi:10.3390/rs11242898
fatcat:maa53wjfbzepllk6rdtguic4ii
Enblending Mosaicked Remote Sensing Images with Spatiotemporal Fusion of Convolutional Neural Networks
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Furthermore, a new spatiotemporal fusion method is proposed by cascading enhanced deep neural networks to fuse images quickly and effectively. ...
Two additional lowresolution reference images are introduced for each mosaicking image. ...
CNNs [40] , deep convolutional spatiotemporal fusion network (DCSTFN) [41] , enhanced DCSTFN (EDCSTFN) [42] , and so on. ...
doi:10.1109/jstars.2021.3082619
fatcat:nmbks2hxjnfczlmsgyy5plhahy
Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning
2021
Animals
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We present a novel nondestructive method with spatiotemporal and motion information based on deep learning for real-time recognition of fish schools' behavior. ...
The model evaluation results on the test dataset further demonstrated that our proposed method could be used as an effective tool for the intelligent perception of fish health status. ...
Acknowledgments: We are grateful for the experimental fish provided by Mingbo Aquatic Products Company in Yantai City, Shandong Province, China. ...
doi:10.3390/ani11102774
pmid:34679796
fatcat:qmir46jr5zho5n46m6epso5eaa
Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning
2020
Journal of Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The spatiotemporal fusion technology for remote sensing data is an effective way to solve the contradiction. ...
In order to improve the accuracy of spatiotemporal fusion, a residual convolution neural network is proposed. ...
In order to achieve the purpose of high-precision spatiotemporal fusion, we use a very deep convolutional network. ...
doi:10.1155/2020/8873079
fatcat:fvzfoebgmbfntas4l2slulswqq
Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network
2018
Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
To address this problem, this paper proposes a new data fusion model named the deep convolutional spatiotemporal fusion network (DCSTFN), which makes full use of a convolutional neural network (CNN) to ...
The features extracted from HTLS and LTHS images are then fused with the aid of an equation that accounts for temporal ground coverage changes. ...
and temporal adaptive reflectance fusion model UBDF Figure 1 . 1 The architecture of the deep convolutional spatiotemporal fusion network (DCSTFN) model. ...
doi:10.3390/rs10071066
fatcat:pk43suejxbcd5j77rgmwpuk6su
Cross Complementary Fusion Network for Video Salient Object Detection
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We present an end-to-end cross complementary network, which consists of a spatiotemporal information learning (STIL) module for spatiotemporal information extraction and a single-image representation enhancement ...
respectively for the representation enhancement and spatiotemporal information learning. ...
Her research interests include visual saliency detection, image segmentation and computer vision. ...
doi:10.1109/access.2020.3036533
fatcat:34wvxqukvvaa3lsl2prao4gaay
A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction and Spatial Channel Attention Mechanism
2022
Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In recent years, spatiotemporal image fusion based on deep learning has received wide attention. ...
The spatiotemporal fusion method is a cost-effective solution for generating a dense temporal data resolution with a high spatial resolution. ...
Acknowledgments: The authors would like to thank all members of Chongqing Key Laboratory of Image Cognition for their kindness and help. ...
doi:10.3390/rs14030461
fatcat:5s36qvzxireibmufythw5ysazm
Spatiotemporal Multi-Task Network for Human Activity Understanding
2017
Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
To tackle these problems, we propose a spatiotemporal, multi-task, 3D deep convolutional neural network to detect (including temporally localize and recognition) actions in untrimmed videos. ...
Then, under the fusion framework, we propose a spatiotemporal multi-task network, which has two sibling output layers for action classification and temporal localization, respectively. ...
To learn spatiotemporal features of videos, an early work [16] extended deep convolutional neural network to three-dimensional, Tran [40] trained deep 3D Convolutional Networks (C3D) on a largescale ...
doi:10.1145/3126686.3126705
dblp:conf/mm/LiuHZCH17
fatcat:vauwsk6ndbd7fhxhel3swqf4fq
Analysis of Behavioral Image Recognition of Pan-Entertainment of Contemporary College Students' Network
2022
Scientific Programming
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
for the weighted fusion of feature maps. ...
Finally, using R(2 + 1)D structure and dual-stream network structure design, a deep learning-based spatiotemporal convolution behavior recognition algorithm is proposed. ...
In the later stage of the deep network part, 3D Conv is again used to perform space-time modeling. e spatiotemporal-r(2 + 1)d end-to-end model proposed in this study performs weight adjustment and fusion ...
doi:10.1155/2022/1176279
fatcat:axja6itxhfhvdnv7xsw2yyjeuy
A Pseudo-siamese Deep Convolutional Neural Network for Spatiotemporal Satellite Image Fusion
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
) for spatiotemporal fusion is proposed in this article. ...
In addition, an attention mechanism is introduced to improve the weight of the crucial information for the remote sensing images. ...
this article designs a new spatiotemporal fusion method for remote sensing images using deep CNN, in which we adopt the network framework design of pseudo-Siamese network and different mechanism blocks ...
doi:10.1109/jstars.2022.3143464
fatcat:wg2cszhqgndafcy3gxlpbqrwle
A Video Expression Recognition Method Based onMulti-mode Convolution Neural Network andMultiplicative Feature Fusion
2021
Journal of Information Processing Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. ...
Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. ...
The model consists of three steps: video preprocessing, deep spatiotemporal expression feature extraction, and fusion expression classification. ...
doi:10.3745/jips.02.0156
dblp:journals/jips/Ren21
fatcat:6sjojaoldfbplmofndumuaaeqi
A Fast Three‐Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset
2020
Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. ...
In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion. ...
Acknowledgments: The authors would like to thank anonymous reviewers for their great comments and suggestions.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs12233888
fatcat:3zovuzktifbhhevls56slz5yai
Dynamic Gesture Recognition Based on Feature Fusion Network and Variant ConvLSTM
2020
IET Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
combines feature fusion network with variant convolutional long short-term memory (ConvLSTM). ...
The architecture extracts spatiotemporal feature information from local, global and deep aspects, and combines feature fusion to alleviate the loss of feature information. ...
Finally, in order to fully mine the deep feature information of figure image, a multi-feature fusion depthwise separable convolution network (MFDSnet) is proposed to extract the deep feature information ...
doi:10.1049/iet-ipr.2019.1248
fatcat:jkoybfiaovbb3cnktsh7mbx4jm
A Multi-cooperative Deep Convolutional Neural Network for Spatiotemporal Satellite Image Fusion
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This paper proposes a multicooperative deep convolutional neural network (MCDNet) for spatiotemporal satellite image fusion. ...
Therefore, spatiotemporal image fusion based on deep learning has received extensive attention in recent years. ...
[28] proposed an unmixing method that uses a deep convolutional neural network for spatiotemporal fusion through two nonlinear mappings, where the relationship between the coarse and fine images is ...
doi:10.1109/jstars.2021.3113163
fatcat:oi3ruyi2u5f3plrgl7eiskgvbi
Pedestrian Behavior Recognition Based on Improved Dual-stream Network with Differential Feature in Surveillance Video
2021
Scientific Programming
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Then, the improved Softmax loss function based on decision-making level feature fusion mechanism is used to train the model, which can retain the spatiotemporal characteristics of images between different ...
Firstly, the deep differential network is used to replace the temporal-stream network so as to improve the representation ability and extraction efficiency of spatiotemporal features. ...
An Improved Dual-Stream Network for Behavior Recognition 3.1. Spatiotemporal Feature Propagation. ...
doi:10.1155/2021/3279957
fatcat:sh6dxvikfzglfoomsxoeig4dgm
« Previous
Showing results 1 — 15 out of 2,928 results