Weiping Li

EAAI Journal 2026 Journal Article

CGMAE: Self-supervised Masked Auto-Encoder with Cross-Graph node alignment for node classification

• Ruoxian Song
• Peng Cao
• Guangqi Wen
• Lanting Li
• Wei Liang
• Weiping Li
• Jinzhu Yang
• Osmar R. Zaiane

YNICL Journal 2020 Journal Article

Functional connectivity markers of depression in advanced Parkinson's disease

• Hai Lin
• Xiaodong Cai
• Doudou Zhang
• Jiali Liu
• Peng Na
• Weiping Li

AAAI Conference 2020 Conference Paper

Region Normalization for Image Inpainting

• Tao Yu
• Zongyu Guo
• Xin Jin
• Shilin Wu
• Zhibo Chen
• Weiping Li
• Zhizheng Zhang
• Sen Liu

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e. g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-ofthe-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.

YNICL Journal 2019 Journal Article

Characterization of white matter changes along fibers by automated fiber quantification in the early stages of Alzheimer's disease

• Xin Zhang
• Yu Sun
• Weiping Li
• Bing Liu
• Wenbo Wu
• Hui Zhao
• Renyuan Liu
• Yue Zhang

AAAI Conference 2018 Conference Paper

Video-Based Sign Language Recognition Without Temporal Segmentation

• Jie Huang
• Wengang Zhou
• Qilin Zhang
• Houqiang Li
• Weiping Li

Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i. e. , isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.

ICRA Conference 1988 Conference Paper

Indirect adaptive robot control

• Weiping Li
• Jean-Jacques E. Slotine

The theoretical issues linked to the development of indirect adaptive robot controllers are discussed, and some possible solutions are proposed. After a review of the prediction models used for robotic parameter estimation, a variety of parameter estimation methods are discussed under a common framework based on an exact solution approach. A novel indirect adaptive controller structure, which consists of a modified computed torque using parameters obtained from any of the estimators discussed, is presented. It is shown that a critical difficulty in using indirect adaptive control is the necessity to explicitly guarantee that the estimated inertia matrix remains positive definite in the course of adaptation, a requirement avoided by both the direct and the composite adaptive controllers. A practical solution to this difficulty is proposed. >

ICRA Conference 1987 Conference Paper

Adaptive manipulator control a case study

• Jean-Jacques E. Slotine
• Weiping Li

Earlier work (Slotine and Li, 1986) exploits the particular structure of manipulator dynamics to develop a simple, globally convergent adaptive algorithm for trajectory control problems. The algorithm does not require measurements or estimates of the manipulator's joint accelerations, nor inversion of the estimated inertia matrix. This paper demonstrates the approach on a high-speed 2 d. o. f. semi-direct-drive robot. It shows that the manipulator mass properties, assumed to be initially unknown, can be precisely estimated within the first half second of a typical run. Similarly, the algorithm allows large loads of unknown mass properties to be precisely manipulated. Further, these experimental results demonstrate that the adaptive controller enjoys essentially the same level of robustness to unmodelled dynamics as a PD controller, yet achieves much better tracking accuracy than either PD or computed-torque schemes. Its superior performance for high speed operations, in the presence of parameter uncertainties, and its relative computational simplicity, make it a attractive option both to address complex industrial tasks, and to simplify high-level programming of more standard operations.

ICRA Conference 1987 Conference Paper

Adaptive strategies in constrained manipulation

• Jean-Jacques E. Slotine
• Weiping Li

Earlier work (Slotine and Li, 1986) demonstrates that using state feedback to directly modify a manipulator's energy function, rather than its fully expanded dynamics, represents a powerful approach to robot control, and, in particular, yields a simple globally convergent adaptive algorithm for trajectory control problems. An important practical feature of the algorithm is that is does not require measurements or estimates of the manipulator's joint accelerations. This paper rewrites the approach in term of end-effector dynamics, extends it to hybrid motion/force control, and discusses adaptive strategies involving mobile environments, such as the external motion control of an unknown passive mechanism.