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Knowledge-Guided Label Distribution Calibration for Federated Affective Computing
IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Early Access Article |
The federated learning (FL) paradigm can significantly solve the rising public concern about data privacy in affective computing. However, conventional FL methods perform poorly due to the uniqueness of the task, as the personalized emotion data vary from client to client. To resolve the privacy-utility paradox, this work proposes a framework that largely improves federated affective computing (FA...Show More
UW-DNeRF: Deformable Soft Tissue Reconstruction with Uncertainty-Guided Depth Supervision and Local Information Integration
Jiwei Shan;Zixin Zhang;Hao Li;Cheng-Tai Hsieh;Yirui Li;Wenhua Wu;Hesheng Wang
IEEE Transactions on Medical Imaging
Year: 2025 | Early Access Article |
Reconstructing deformable soft tissues from endoscopic videos is a critical yet challenging task. Leveraging depth priors, deformable implicit neural representations have seen significant advancements in this field. However, depth priors from pre-trained depth estimation models are often coarse, and inaccurate depth supervision can severely impair the performance of these neural networks. Moreover...Show More
Multiple load deviation compensation method of distribution network under new power system
Zixin Zhang;Jingjiao Yin;Zhentao Han;Xiaoyi Huang;Qixiang Wang
2023 4th International Conference on Smart Grid and Energy Engineering (SGEE)
Year: 2023 | Conference Paper |
In the new power system, due to the popularity of new energy technologies such as distributed energy and electric vehicles, load changes are more diversified and unpredictable. Therefore, this paper proposes a compensation method for diversified load deviation of distribution network under new power system. The zero-crossing detection method is used to solve the signal frequency value, and the loa...Show More
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
Wentao Hu;Jia Zheng;Zixin Zhang;Xiaojun Yuan;Jian Yin;Zihan Zhou
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practic...Show More
Multi-IMU Proprioceptive Odometry for Legged Robots
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
This paper presents a novel, low-cost proprioceptive sensing solution for legged robots with point feet to achieve accurate low-drift long-term position and velocity estimation. In addition to conventional sensors, including one body Inertial Measurement Unit (IMU) and joint encoders, we attach an additional IMU to each calf link of the robot just above the foot. An extended Kalman filter is used ...Show More
Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion
2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Cited by: Papers (16)
We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution for legged robots, called Cerberus, which precisely estimates position on various terrains in real-time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we perform online kinematic parameter calibration and outlier rejec...Show More
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