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Haipeng Li

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Time Series Class-Incremental Learning via Confidence-guided Mask Distillation and Prototype-guided Contrastive Learning

Yu Liu
Haoqin Yang
Jinping Sui
Hui Wang
Haipeng Li
Weimin Wang
Qi Jia

Class-incremental learning (CIL) has recently gained great attention in the field of time series classification. Existing CIL methods based on knowledge distillation exhibit impressive ability to retain prior knowledge and overcome catastrophic forgetting, however, their effectiveness faces major challenges posed by time series data. Since temporal data is more susceptible to sensor errors and electronic noise, the distillation process may be significantly affected by noisy knowledge transfer. To address this issue, we propose a novel confidence-guided mask distillation (CMD) framework, to prevent the noisy inheritance during distillation. The core of CMD lies in a dynamic masking mechanism guided by prediction confidence, capable of allocating higher weights to high-confidence time series and substantially suppressing the influence of low-confidence ones. Additionally, different from prior work simply passing a set of feature prototypes to the classifier, we develop prototype-guided contrastive learning (PCL) to alleviate the classifier bias on new classes, through extra contrastive constraints to push away the feature distributions of old feature prototypes from those of new classes features. Extensive experiments on three time-series datasets demonstrate that, our method significantly outperforms other replay-free CIL approaches in raising average accuracy, as well as decreasing forgetting rate.

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AAAI Conference 2025 Conference Paper

HybridReg: Robust 3D Point Cloud Registration with Hybrid Motions

Keyu Du
Hao Xu
Haipeng Li
Hong Qu
Chi-Wing Fu
Shuaicheng Liu

Scene-level point cloud registration is very challenging when considering dynamic foregrounds. Existing indoor datasets mostly assume rigid motions, so the trained models cannot robustly handle scenes with non-rigid motions. On the other hand, non-rigid datasets are mainly object-level, so the trained models cannot generalize well to complex scenes. This paper presents HybridReg, a new approach to 3D point cloud registration, learning uncertainty mask to account for hybrid motions: rigid for backgrounds and non-rigid/rigid for instance-level foregrounds. First, we build a scene-level 3D registration dataset, namely HybridMatch, designed specifically with strategies to arrange diverse deforming foregrounds in a controllable manner. Second, we account for different motion types and formulate a mask-learning module to alleviate the interference of deforming outliers. Third, we exploit a simple yet effective negative log-likelihood loss to adopt uncertainty to guide the feature extraction and correlation computation. To our best knowledge, HybridReg is the first work that exploits hybrid motions for robust point cloud registration. Extensive experiments show HybridReg's strengths, leading it to achieve state-of-the-art performance on both widely-used indoor and outdoor datasets.

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AAAI Conference 2025 Conference Paper

Single Image Rolling Shutter Removal with Diffusion Models

Zhanglei Yang
Haipeng Li
Mingbo Hong
Chen-Lin Zhang
Jiajun Li
Shuaicheng Liu

We present RS-Diffusion, the first Diffusion Models-based method for single-frame Rolling Shutter (RS) correction. RS artifacts compromise visual quality of frames due to the row-wise exposure of CMOS sensors. Most previous methods have focused on multi-frame approaches, using temporal information from consecutive frames for the motion rectification. However, few approaches address the more challenging but important single frame RS correction. In this work, we present an ``image-to-motion" framework via diffusion techniques, with a designed patch-attention module. In addition, we present the RS-Real dataset, comprised of captured RS frames alongside their corresponding Global Shutter (GS) ground-truth pairs. The GS frames are corrected from the RS ones, guided by the corresponding Inertial Measurement Unit (IMU) gyroscope data acquired during capture. Experiments show that RS-Diffusion surpasses previous single-frame RS methods, demonstrates the potential of diffusion-based approaches, and provides a valuable dataset for further research.

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AAAI Conference 2023 Conference Paper

Semi-supervised Deep Large-Baseline Homography Estimation with Progressive Equivalence Constraint

Hai Jiang
Haipeng Li
Yuhang Lu
Songchen Han
Shuaicheng Liu

Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.

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IROS Conference 2014 Conference Paper

A sequence of micro-assembly for irregular objects based on a multiple manipulator platform

Dengpeng Xing
De Xu
Haipeng Li

Difficulties arise in the micro-assembly of many irregular objects and in the insertion with contact between components of soft materials. To handle these problems, we design a micro-operational platform with multiple manipulators to facilitate a sequence of assembly. Six robot arms and three microscopes are incorporated, together with macro and micro motion systems. We also propose a hybrid control strategy to achieve high precision and protect objects. This hybrid scheme includes vision based positioning controllers for alignment, which employ incremental PI controllers and image Jacobian matrix, force based controllers for insertion, and a decision mechanism determining the assembly state. Experiments demonstrate the effectiveness of the proposed platform and control methods.

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ICRA Conference 2014 Conference Paper

Active calibration and its applications on micro-operating platform with multiple manipulators

Dengpeng Xing
De Xu
Haipeng Li
Liyan Luo

The microscope has characteristics of a planar vision with small view field and small view depth. For micro operation systems with multiple manipulators, the handling of irregular objects may lead to a nonorthogonal microscopic system, which needs to focus on clear viewing interested features, and it may also hardly locate the exact position and posture of the robot arms. In view of these, this paper proposes an active calibration method to compute image Jacobian matrix, which maps from the relative motion of the manipulators to the image coordination changes in the microscopes. We also investigate the applications in micro operator positioning, tracking for distributed systems, and movement optimization in micro-assembly. Experiments are carried out on a micro-assembly platform equipped with three microscopes and six robot arms, and the results validate the effectiveness of the proposed method.

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