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Found 13 papers, 3 papers with code
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency.
Masked Cross-image Encoding for Few-shot Segmentation
Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images.
Ranked #24 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Joint Radio Frequency Fingerprints Identification via Multi-antenna Receiver
When the number is small, the Mutual Information Weighting Scheme (MIWS) is developed by calculating the weighted voting of RFFI result at each antenna; when the number is moderate, the Distortions Filtering Scheme (DFS) is developed by filtering out the channel noise and receiver distortions; when the number is large enough, the Group-Distortions Filtering and Weighting Scheme (GDFWS) is developed, which integrates the advantages of MIWS and DFS.
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark.
Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance Fields
Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively.
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark
To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving.
Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound.
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares
In this paper, we propose a blind-block orthogonal least squares-based compressive spectrum sensing (B-BOLS-CSS) algorithm, which utilizes a novel blind stopping rule to cut the cords to these prior information.
Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain
In frequency division duplex (FDD) massive MIMO systems, reliable downlink channel estimation is essential for the subsequent data transmission but is realized at the cost of massive pilot overhead due to hundreds of antennas at base station (BS).
Efficient Wideband DoA Estimation with a Robust Iterative Method for Uniform Circular Arrays
The key parameters of the proposed method in the current iteration are adjusted based on the estimation results in the previous iterations.
Blind Orthogonal Least Squares based Compressive Spectrum Sensing
As an enabling technique of cognitive radio (CR), compressive spectrum sensing (CSS) based on compressive sensing (CS) can detect the spectrum opportunities from wide frequency bands efficiently and accurately by using sub-Nyquist sampling rate.
Multilayer Graph Contrastive Clustering Network
To this end, we propose a generic and effective autoencoder framework for multilayer graph clustering named Multilayer Graph Contrastive Clustering Network (MGCCN).
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.
