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Found 16 papers, 4 papers with code
DFR-Net: Density Feature Refinement Network for Image Dehazing Utilizing Haze Density Difference
In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them closer to clear image features.
FoSp: Focus and Separation Network for Early Smoke Segmentation
We first introduce a Focus module employing bidirectional cascade which guides low-resolution and high-resolution features towards mid-resolution to locate and determine the scope of smoke, reducing the miss detection rate.
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels.
Object Preserving Siamese Network for Single Object Tracking on Point Clouds
Exploring an approach that seeks to maximize the preservation of object points and their object-aware features is of particular significance.
Dynamic Background Reconstruction via MAE for Infrared Small Target Detection
Background reconstruction is one of the methods to deal with this problem.
CourtNet for Infrared Small-Target Detection
To deal with this difficulty, this paper proposes a neural-network-based ISTD method called CourtNet, which has three sub-networks: the prosecution network is designed for improving the recall rate; the defendant network is devoted to increasing the precision rate; the jury network weights their results to adaptively balance the precision and recall rate.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.
Theoretical Analysis of Deep Neural Networks in Physical Layer Communication
In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity.
DRPN: Making CNN Dynamically Handle Scale Variation
In this paper, we propose a dynamic re-parameterization network (DRPN) to deal with the scale variation and balance the detection precision between small targets and large targets in infrared datasets.
Dynamic Fusion Network for RGBT Tracking
The key points of RGBT tracking lie in feature extraction and feature fusion of visible and infrared images.
Ranked #8 on
Rgb-T Tracking
on GTOT
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Opening the Black Box of Deep Neural Networks in Physical Layer Communication
Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems.
Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks
Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks.
A Cyber Physical System Framework for UAV Communications
Diverse applications have witnessed the prevalence of unmanned aerial vehicles (UAVs) due to their agility and versatility.
Attention-based Context Aggregation Network for Monocular Depth Estimation
Second, the proposed ACAN aggregates both the image-level and pixel-level context information for depth estimation, where the former expresses the statistical characteristic of the whole image and the latter extracts the long-range spatial dependencies for each pixel.
Ranked #51 on
Monocular Depth Estimation
on NYU-Depth V2
Neural Component Analysis for Fault Detection
Since PCA-based methods assume that the monitored process is linear, nonlinear PCA models, such as autoencoder models and kernel principal component analysis (KPCA), has been proposed and applied to nonlinear process monitoring.
