10 citations · 17 across the 10 of their papers we have counts for
10 papers
A Transductive Maximum Margin Classifier for Few-Shot Learning
Fei Pan, Chunlei Xu, Jie Guo +1
Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled examples per class are given. We introduce a transductive maximum margin c…
Temporal Alignment Prediction for Few-Shot Video Classification
Fei Pan, Chunlei Xu, Jie Guo +1
The goal of few-shot video classification is to learn a classification model with good generalization ability when trained with only a few labeled videos. However, it is difficult…
Quantity over Quality: Dithered Quantization for Compressive Radar Systems
Thomas Feuillen, Chunlei Xu, Jérôme Louveaux +2
In this paper, we investigate a trade-off between the number of radar observations (or measurements) and their resolution in the context of radar range estimation. To this end, we…
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing
Thomas Feuillen, Chunlei Xu, Luc Vandendorpe +1
We present a novel scheme allowing for 2D target localization using highly quantized 1-bit measurements from a Frequency Modulated Continuous Wave (FMCW) radar with two receiving a…
Taking the edge off quantization: projected back projection in dithered compressive sensing
Chunlei Xu, Vincent Schellekens, Laurent Jacques
Quantized compressive sensing (QCS) deals with the problem of representing compressive signal measurements with finite precision representation, i.e., a mandatory process in any pr…
Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering
Chunlei Xu, Laurent Jacques
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a ma…