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Xinrong Chen

7 accepted papers

2026

KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse Signals

AAAI 2026technical

Full-body motion tracking plays an essential role in AR/VR applications, bridging physical and virtual interactions. However, it is challenging to reconstruct realistic and diverse full-body poses based on sparse signals obtained by head-mounted displays, which are the main devices in AR/VR scenario

Cited by 0SourcePDFScholar
2026

Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance

CVPR 2026

Large Vision-Language Models (LVLMs) can reason from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent,

Cited by 0SourceScholar
2025

EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training

IROS 2025

Monocular depth estimation and ego-motion estimation are significant tasks for scene perception and navigation in stable, accurate and efficient robot-assisted endoscopy. To tackle lighting variations and sparse textures in endoscopic scenes, multiple techniques including optical flow, appearance fl

Cited by 1SourcecodeScholar
2025

GuiLoMo: Allocating Experts and Ranks for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors

EMNLP 2025

Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs. However, their performance is limited by the small number of trainable parameters. Recent work combines LoRA with the Mixtu

2025

Remote: Real-Time Ego-Motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning

ICRA 2025

Real-time ego-motion tracking for endoscope is a significant task for efficient navigation and robotic automation of endoscopy. In this paper, a novel framework is proposed to perform real-time ego-motion tracking for endoscope. Firstly, a multi-modal visual feature learning network is proposed to p

Cited by 0SourceScholar
2024

SPTESleepNet: Automatic Sleep Staging Model Based On Strip Patch Embeddings And Transformer Encoder

ICASSP 2024accepted

Although the research on automatic sleep staging has made great progress, there is still a certain distance from its clinical application. For a machine scoring system, in order to work in an interactive and collaborative manner with practitioners, two barriers need to be addressed, which are high a…

Cited by 0SourceScholar
2022

PointCLM: A Contrastive Learning-Based Framework for Multi-Instance Point Cloud Registration

ECCV 2022poster

"Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud. Solving this problem is challenging since inlier correspondences of one instance constitute outliers of all the other instances. Existing methods often re…