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Found 17 papers, 2 papers with code
GT-Rain Single Image Deraining Challenge Report
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
FRC-GIF: Frame Ranking-based Personalized Artistic Media Generation Method for Resource Constrained Devices
Generating video highlights in the form of animated graphics interchange formats (GIFs) has significantly simplified the process of video browsing.
FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer
In this paper, we propose a FusIon of STyles (FIST) network for facial images that leverages pre-trained multipath style transfer networks to eliminate the problem associated with lack of huge data volume in the training phase along with the fusion of multiple styles at the output.
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT
We perform our experimental analysis on publicly available medical imaging datasets and show that our proposed SelfFed framework performs better when compared to existing baselines concerning non-independent and identically distributed (IID) data and label scarcity.
ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review
Furthermore, we also suggest some policies and recommendations for EU AI policy act concerning ethics, digital divide, and sustainability.
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks
Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning.
2nd Place Solutions for UG2+ Challenge 2022 -- D$^{3}$Net for Mitigating Atmospheric Turbulence from Images
This technical report briefly introduces to the D$^{3}$Net proposed by our team "TUK-IKLAB" for Atmospheric Turbulence Mitigation in $UG2^{+}$ Challenge at CVPR 2022.
AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends
It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G
We propose an informative instance-based fusion network (IIFNet) to cope with random noise and to improve detection performance, simultaneously.
Towards Energy Efficient Distributed Federated Learning for 6G Networks
The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks.
Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data
However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis.
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform
Using two medical datasets, we validate our proposed SR network against existing state-of-the-art approaches and achieve promising results in terms of SSIM and PSNR.
Towards Industrial Private AI: A two-tier framework for data and model security
We proposed a three-layer encryption method for data security and provide a hypothetical method to secure the model parameters.
Internet of Everything enabled solution for COVID-19, its new variants and future pandemics: Framework, Challenges, and Research Directions
After affecting the world in unexpected ways, COVID-19 has started mutating which is evident with the insurgence of its new variants.
Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks
The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features.
Semantic Image Networks for Human Action Recognition
We also propose the sequential combination of Inception-ResNetv2 and long-short-term memory network (LSTM) to leverage the temporal variances for improved recognition performance.
