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Yimin Liu

21 accepted papers

2026

Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning

AAAI 2026technical

Graph-based vertical federated learning (GVFL) enables multiple parties to collaboratively train and infer over aligned nodes, where each party contributes its own local embedding derived from different attributes and adjacency relations. Adversarial inputs injected by an attacker can skew the joint

Cited by 0SourcePDFScholar
2026

SANER: Switchable Adapter with Non-parametric Enhanced Routing for Person De-Reidentification

CVPR 2026

Person De-Reidentification (De-ReID) is an emerging and safety-critical task that aims to selectively forget specific individuals in surveillance systems while preserving the recognition capability for others. Existing methods typically learn both forgetting and retaining objectives within a unified

Cited by 0SourcecodeScholar
2024

Fundamental Limits of Direction Finding in Distributed Arrays Exploiting Auxiliary Sources

ICASSP 2024accepted

We consider the problem of estimating the directions of multiple target sources by exploiting auxiliary sources, focusing on a single snapshot obtained by the distributed array with position errors and angular offsets of subarrays. Former calibration methods generally assume the directions of auxili…

Cited by 0SourceScholar
2024

RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation

AAAI 2024technical

Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital capabilities for mobile systems to achieve full autonomy. Several approaches have attempted to achieve MOSEVE using a LiDAR sensor. However, LiDAR sensors are typically expensive and susceptible to adverse weather condition…

2022

Transmit Beamforming with Fixed Covariance for Integrated MIMO Radar and Multiuser Communications

ICASSP 2022accepted

In this paper, we consider the design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In contrast to the previous designs which guarantee communication performance, we require the covarian…

Cited by 0SourceScholar
2021

A New Automotive Radar 4D Point Clouds Detector by Using Deep Learning

ICASSP 2021accepted

The millimeter-wave radar, as an important sensor, is widely used in autonomous driving. In recent years, to meet the requirement of high level autonomous driving applications, attentions have been paid to generate high-quality radar point clouds. However, in the complex roadway environment, the wea…

Cited by 0SourceScholar
2021

Are We Ready for Unmanned Surface Vehicles in Inland Waterways? The USVInland Multisensor Dataset and Benchmark

RA-L 2021

Unmanned surface vehicles (USVs) have great value with their ability to execute hazardous and time-consuming missions over water surfaces. Recently, USVs for inland waterways have attracted increasing attention for their potential application in autonomous monitoring, transportation, and cleaning. H

Cited by 119SourceScholar
2021

Bit Constrained Communication Receivers In Joint Radar Communications Systems

ICASSP 2021accepted

Dual function radar and communications (DFRC) systems are the focus of growing research attention. The common DFRC setup considers simultaneous probing and information transmission to a remote receiver, typically involving complex radar-oriented waveforms, whose detection can induce a notable burden…

Cited by 0SourceScholar
2021

FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

ICCV 2021poster

Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from getting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The c…

Cited by 115PDFcodeScholar
2021

Robust Small Object Detection on the Water Surface Through Fusion of Camera and Millimeter Wave Radar

ICCV 2021poster

In recent years, unmanned surface vehicles (USVs) have been experiencing growth in various applications. With the expansion of USVs' application scenes from the typical marine areas to inland waters, new challenges arise for the object detection task, which is an essential part of the perception sys…

Cited by 79PDFcodeScholar
2020

Complexity Reduction Methods for Index Modulation Based Dual-Function Radar Communication Systems

ICASSP 2020accepted

Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware. An emerging DFRC strategy embeds transmission of digital messages into agility-based radar schemes in the form of index modulation (IM). This approach provides the ability to communicat…

Cited by 0SourceScholar
2020

Theoretical Analysis of Multi-Carrier Agile Phased Array Radar

ICASSP 2020accepted

Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manner. To enhance the target recovery performance…

Cited by 0SourceScholar
2018

A Novel Joint Radar and Communication System Based on Randomized Partition of Antenna Array

ICASSP 2018accepted

Partitioning the antenna array into different subarrays is a flexible scheme in the joint radar and communication system. However, the traditional fixed partition of the antenna array cannot make full use of the complete aperture. In this paper, we propose a novel antenna partition scheme. In this s…

Cited by 0SourceScholar
2016

Group sparse Bayesian learning via exact and fast marginal likelihood maximization

ICASSP 2016accepted

This paper concerns sparse Bayesian learning (SBL) problem for group sparse signals. Group sparsity means that the signal components can be divided into groups, and the entries in one group are simultaneously zero or nonzero. In SBL, each group is controlled by a hyper-parameter. The marginal likeli…

Cited by 0SourceScholar