13 citations · 19 across the 10 of their papers we have counts for
8 papers · 1 filter
KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse Signals
Shuting Zhao, Zeyu Xiao, Xinrong Chen
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…
EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes
Liangjing Shao, Chenkang Du, Benshuang Chen +2
Self-supervised monocular depth estimation is a significant task for low-cost and efficient 3D scene perception and measurement in endoscopy. However, the variety of illumination c…
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training
Liangjing Shao, Linxin Bai, Chenkang Du +1
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…
SSD-Poser: Avatar Pose Estimation with State Space Duality from Sparse Observations
Shuting Zhao, Linxin Bai, Liangjing Shao +2
The growing applications of AR/VR increase the demand for real-time full-body pose estimation from Head-Mounted Displays (HMDs). Although HMDs provide joint signals from the head a…
REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning
Liangjing Shao, Benshuang Chen, Shuting Zhao +1
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 perf…
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
Shuting Zhao, Chenkang Du, Kristin Qi +2
Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently. However, such approaches typically under-perform training since they limi…
Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training
Yingfan Ma, Xiaoyuan Luo, Mingzhi Yuan +2
Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information conta…
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Haoqin Ji, Haozhe Liu, Yuexiang Li +7
Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed…