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Found 18 papers, 2 papers with code
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
However, we observe that the non-IID data in federated learning leads to a gap in performance between the PEFT method and full parameter fine-tuning(FFT).
Augmented Labeled Random Finite Sets and Its Application to Group Target Tracking
Furthermore, by means of the labeled multi-Bernoulli (LMB) filter with the proposed augmented LRFSs, the group structure is iteratively propagated and updated during the tracking process, which achieves the simultaneously estimation of the kinetic states, track label, and the corresponding group information of multiple group targets, and further improves the GTT tracking performance.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images.
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training.
The Model Inversion Eavesdropping Attack in Semantic Communication Systems
In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency.
WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for Fast Multispectral Pedestrian Detection
Multispectral pedestrian detection achieves better visibility in challenging conditions and thus has a broad application in various tasks, for which both the accuracy and computational cost are of paramount importance.
Semantic-aware Transmission for Robust Point Cloud Classification
As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing.
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives
Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects.
Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission
Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years.
HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar
Gait recognition is widely used in diversified practical applications.
Ziv-Zakai Bound for DOAs Estimation
By contrast, the existing global tight Ziv-Zakai bound (ZZB) is appropriate for evaluating the single source estimation only.
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation
In an asynchronous federated learning framework, the server updates the global model once it receives an update from a client instead of waiting for all the updates to arrive as in the synchronous setting.
Semantic-preserved Communication System for Highly Efficient Speech Transmission
Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years.
Global and Asymptotically Efficient Localization from Range Measurements
In terms of whether the variance of measurement noises is known or not, we propose the Bias-Eli estimator (which involves solving a generalized trust region subproblem) and the Noise-Est estimator (which is obtained by solving a convex problem), respectively.
Semantic-aware Speech to Text Transmission with Redundancy Removal
We also propose a two-stage training scheme, which speeds up the training of the proposed DL model.
FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devices
Our numerical results demonstrate that FTPipeHD is 6. 8x faster in training than the state of the art method when the computing capacity of the best device is 10x greater than the worst one.
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
In this paper, a Doppler Radar based hand gesture recognition system using convolutional neural networks is proposed.
Deformable Deep Convolutional Generative Adversarial Network in Microwave Based Hand Gesture Recognition System
Traditional vision-based hand gesture recognition systems is limited under dark circumstances.
BIG-bench Machine Learning
Generative Adversarial Network
+2
