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Chen Zhong

5 accepted papers

2026

An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling

AAAI 2026technical

Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In t

Cited by 0SourcePDFScholar
2026

Learning from Disagreement: A Group Decision Simulation Framework for Robust Medical Image Segmentation

ICASSP 2026poster

Medical image segmentation annotation suffers from inter-rater variability (IRV) due to differences in annotators' expertise and the inherent blurriness of medical images. Standard approaches that simply average expert labels are flawed, as they discard the valuable clinical uncertainty revealed in…

Cited by 0SourcePDFScholar
2025

Cooperative Multi-Target Tracking Based on Multi-Detection TPHD in MIMO-OFDM Systems

ICASSP 2025accepted

This paper presents a passive multiple trajectories tracking system with multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals transmitted by the base station (BS). Firstly, we propose a Bayesian learning method to obtain the coarse estimations of targets and clutte…

Cited by 0SourceScholar
2024

Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving

IROS 2024poster

Motion prediction is among the most fundamental tasks in autonomous driving. Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn…

Cited by 13SourcecodeScholar
2022

Controlled Sensing and Anomaly Detection Via Soft Actor-Critic Reinforcement Learning

ICASSP 2022accepted

To address the anomaly detection problem in the presence of noisy observations and to tackle the tuning and efficient exploration challenges that arise in deep reinforcement learning algorithms, we in this paper propose a soft actor-critic deep reinforcement learning framework. To evaluate the propo…

Cited by 0SourceScholar