Edge computing driven low-light image dynamic enhancement for object detection
Y Wu, H Guo, C Chakraborty… - … on Network Science …, 2022 - ieeexplore.ieee.org
With fast increase in volume of mobile multimedia data, how to apply powerful deep learning
methods to process data with real-time response becomes a major issue. Meanwhile, edge …
methods to process data with real-time response becomes a major issue. Meanwhile, edge …
An overview of knowledge graph reasoning: key technologies and applications
In recent years, with the rapid development of Internet technology and applications, the
scale of Internet data has exploded, which contains a significant amount of valuable …
scale of Internet data has exploded, which contains a significant amount of valuable …
An attention based dual learning approach for video captioning
Video captioning aims to generate sentences/captions to describe video contents. It is one of
the key tasks in the field of multimedia processing. However, most of the current video …
the key tasks in the field of multimedia processing. However, most of the current video …
A multi-instance multi-label dual learning approach for video captioning
W Ji, R Wang - ACM Transactions on Multimidia Computing …, 2021 - dl.acm.org
Video captioning is a challenging task in the field of multimedia processing, which aims to
generate informative natural language descriptions/captions to describe video contents …
generate informative natural language descriptions/captions to describe video contents …
ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection
Recently, people around the world are being vulnerable to the pandemic effect of the novel
Corona Virus. It is very difficult to detect the virus infected chest X-ray (CXR) image during …
Corona Virus. It is very difficult to detect the virus infected chest X-ray (CXR) image during …
Nuclei probability and centroid map network for nuclei instance segmentation in histology images
Nuclei instance segmentation is an integral step in digital pathology workflow as it is a
prerequisite for most downstream tasks such as patient survival analysis, precision …
prerequisite for most downstream tasks such as patient survival analysis, precision …
Bayesian estimation‐based sentiment word embedding model for sentiment analysis
Sentiment word embedding has been extensively studied and used in sentiment analysis
tasks. However, most existing models have failed to differentiate high‐frequency and low …
tasks. However, most existing models have failed to differentiate high‐frequency and low …
HighBoostNet: a deep light-weight image super-resolution network using high-boost residual blocks
Image distortion is an inevitable part of the image acquisition process, which negatively
affects the high-frequency contents of the images. Therefore, it is important to improve the …
affects the high-frequency contents of the images. Therefore, it is important to improve the …
GCPAN: an adaptive global cross-scale prior attention network for image super-resolution
M Shi, S Kong, B Zao, M Tan - Neural Computing and Applications, 2023 - Springer
Super-resolution has achieved remarkable results in recent years, which is attributed to the
rapid development of convolutional neural networks (CNN). However, most CNN-based …
rapid development of convolutional neural networks (CNN). However, most CNN-based …
Quasi/Periodic Noise Reduction in Images Using Modified Multiresolution-Convolutional Neural Networks for 3D Object Reconstructions and Comparison with Other …
OA Espinosa-Bernal, JC Pedraza-Ortega… - Computers, 2024 - mdpi.com
The modeling of real objects digitally is an area that has generated a high demand due to
the need to obtain systems that are able to reproduce 3D objects from real objects. To this …
the need to obtain systems that are able to reproduce 3D objects from real objects. To this …