54 citations · 71 across the 14 of their papers we have counts for
14 papers
EPSegFZ: Efficient Point Cloud Semantic Segmentation for Few- and Zero-Shot Scenarios with Language Guidance
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage…
SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail…
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely…
Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering
Jay Karhade, Haiyue Zhu, Ka-Shing Chung +3
We present a neural point cloud rendering pipeline through a novel multi-frequency-aware patch adversarial learning framework. The proposed approach aims to improve the rendering r…
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
Jiahui Wang, Haiyue Zhu, Haoren Guo +3
3D part segmentation is an essential step in advanced CAM/CAD workflow. Precise 3D segmentation contributes to lower defective rate of work-pieces produced by the manufacturing equ…
Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools
Haoren Guo, Haiyue Zhu, Jiahui Wang +3
Prediction of Remaining Useful Lifetime(RUL) in the modern manufacturing and automation workplace for machines and tools is essential in Industry 4.0. This is clearly evident as co…
Incremental Few-Shot Learning via Implanting and Compressing
Yiting Li, Haiyue Zhu, Xijia Feng +5
This work focuses on tackling the challenging but realistic visual task of Incremental Few-Shot Learning (IFSL), which requires a model to continually learn novel classes from only…
Towards Generalized and Incremental Few-Shot Object Detection
Yiting Li, Haiyue Zhu, Jun Ma +4
Real-world object detection is highly desired to be equipped with the learning expandability that can enlarge its detection classes incrementally. Moreover, such learning from only…
Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System
Jun Ma, Zilong Cheng, Haiyue Zhu +3
Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features. The technology offers high-…
Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation
Haiyue Zhu, Yiting Li, Fengjun Bai +6
Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e.g., in grasping detection, the inference skill learned from a grasping da…
Data-Driven Multi-Objective Controller Optimization for a Magnetically-Levitated Nanopositioning System
Xiaocong Li, Haiyue Zhu, Jun Ma +4
The performance achieved with traditional model-based control system design approaches typically relies heavily upon accurate modeling of the motion dynamics. However, modeling the…
Robust Fixed-Order Controller Design for Uncertain Systems with Generalized Common Lyapunov Strictly Positive Realness Characterization
Jun Ma, Haiyue Zhu, Xiaocong Li +3
This paper investigates the design of a robust fixed-order controller for single-input-single-output (SISO) polytopic systems with interval uncertainties, with the aim that the clo…
Incremental Few-Shot Object Detection for Robotics
Yiting Li, Haiyue Zhu, Sichao Tian +6
Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated…
On Robust Stability and Performance with a Fixed-Order Controller Design for Uncertain Systems
Jun Ma, Haiyue Zhu, Masayoshi Tomizuka +1
Typically, it is desirable to design a control system that is not only robustly stable in the presence of parametric uncertainties but also guarantees an adequate level of system p…