Author name cluster

Liang Peng

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

16 papers

2 author rows

AAAI Conference 2026 Conference Paper

Finding Time Series Anomalies Using Granular-Ball Vector Data Description

Lifeng Shen
Liang Peng
Ruiwen Liu
Shuyin Xia
Yi Liu

Modeling normal behavior in dynamic, nonlinear time series data is challenging for effective anomaly detection. Traditional methods, such as nearest neighbor and clustering approaches, often depend on rigid assumptions, such as a predefined number of reliable neighbors or clusters, which frequently break down in complex temporal scenarios. To address these limitations, we introduce the Granular-ball One-Class Network (GBOC), a novel approach based on a data-adaptive representation called Granular-ball Vector Data Description (GVDD). GVDD partitions the latent space into compact, high-density regions represented by granular-balls, which are generated through a density-guided hierarchical splitting process and refined by removing noisy structures. Each granular-ball serves as a prototype for local normal behavior, naturally positioning itself between individual instances and clusters while preserving the local topological structure of the sample set. During training, GBOC improves the compactness of representations by aligning samples with their nearest granular-ball centers. During inference, anomaly scores are computed based on the distance to the nearest granular-ball. By focusing on dense, high-quality regions and significantly reducing the number of prototypes, GBOC delivers both robustness and efficiency in anomaly detection. Extensive experiments validate the effectiveness and superiority of the proposed method, highlighting its ability to handle the challenges of time series anomaly detection.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Refinement Contrastive Learning of Cell–Gene Associations for Unsupervised Cell Type Identification

Liang Peng
Haopeng Liu
Yixuan Ye
Cheng Liu
Wenjun Shen
Si Wu
Hau-San Wong

Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single-cell RNA-seq and spatial transcriptomics benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene-expression signatures, further validating the biological relevance of our approach.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

GLDiTalker: Speech-Driven 3D Facial Animation with Graph Latent Diffusion Transformer

Yihong Lin
Zhaoxin Fan
Xianjia Wu
Lingyu Xiong
Xiandong Li
Wenxiong Kang
Liang Peng
Songju Lei

Speech-driven talking head generation is a critical yet challenging task with applications in augmented reality and virtual human modeling. While recent approaches using autoregressive and diffusion-based models have achieved notable progress, they often suffer from modality inconsistencies, particularly misalignment between audio and mesh, leading to reduced motion diversity and lip-sync accuracy. To address this, we propose GLDiTalker, a novel speech-driven 3D facial animation model based on a Graph Latent Diffusion Transformer. GLDiTalker resolves modality misalignment by diffusing signals within a quantized spatiotemporal latent space. It employs a two-stage training pipeline: the Graph-Enhanced Quantized Space Learning Stage ensures lip-sync accuracy, while the Space-Time Powered Latent Diffusion Stage enhances motion diversity. Together, these stages enable GLDiTalker to generate realistic, temporally stable 3D facial animations. Extensive evaluations on standard benchmarks demonstrate that GLDiTalker outperforms existing methods, achieving superior results in both lip-sync accuracy and motion diversity.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Local Conditional Controlling for Text-to-Image Diffusion Models

Yibo Zhao
Liang Peng
Yang Yang
Zekai Luo
Hengjia Li
Yao Chen
Zheng Yang
Xiaofei He

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we explore a novel and practical task setting: local control. It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt. However, it is non-trivial to achieve it. The naive manner of directly adding local conditions may lead to the local control dominance problem, which forces the model to focus on the controlled region and neglect object generation in other regions. To mitigate this problem, we propose Regional Discriminate Loss to update the noised latents, aiming at enhanced object generation in non-control regions. Furthermore, the proposed Focused Token Response suppresses weaker attention scores which lack the strongest response to enhance object distinction and reduce duplication. Lastly, we adopt Feature Mask Constraint to reduce quality degradation in images caused by information differences across the local control region. All proposed strategies are operated at the inference stage. Extensive experiments demonstrate that our method can synthesize high-quality images aligned with the text prompt under local control conditions.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

MCD-CLIP: Multi-view Chest Disease Diagnosis with Disentangled CLIP

Songyue Cai
Yujie Mo
Liang Peng
Yucheng Xie
Tao Tong
Xiaofeng Zhu

Pre-trained methods for multi-view chest X-ray images have demonstrated impressive performance in chest disease diagnosis, but there are still some limitations that need to be addressed. Firstly, many pre-trained methods require full fine-tuning pre-trained models to induce significant computational resource usage and the prior knowledge destruction. Secondly, many pre-trained methods cannot efficiently balance consistency and complementarity among views, leading to information loss and performance degradation. To tackle these issues, we propose MCD-CLIP, a CLIP-based multi-view chest disease diagnosis method. It uses visual prompts and a Prompt-Aligner to align prompts across views, along with the additional text representation for efficient transfer. Moreover, we employ Adapters to disentangle the image representation, maintaining consistency and complementarity from different views. Experimental results on the chest X-ray dataset demonstrate that MCD-CLIP achieves comparable or better performance on a variety of tasks with 94. 31% fewer tunable parameters compared to state-of-the-art methods. The source codes are released at https: //github. com/YuzunoKawori/MCD-CLIP.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Self-Supervised Direct Preference Optimization for Text-to-Image Diffusion Models

Liang Peng
Boxi Wu
Haoran Cheng
Yibo Zhao
Xiaofei He

Direct preference optimization (DPO) is an effective method for aligning generative models with human preferences and has been successfully applied to fine‑tune text‑to‑image diffusion models. Its practical adoption, however, is hindered by a labor‑intensive pipeline that first produces a large set of candidate images and then requires humans to rank them pairwise. We address this bottleneck with self‑supervised direct preference optimization, a new paradigm that removes the need for any pre‑generated images or manual ranking. During training, we create preference pairs on the fly through self‑supervised image transformations, allowing the model to learn from fresh and diverse comparisons at every iteration. This online strategy eliminates costly data collection and annotation while remaining plug‑and‑play for any text‑to‑image diffusion method. Surprisingly, the on‑the‑fly pairs produced by the proposed method not only match but exceed the effectiveness of conventional DPO, which we attribute to the greater diversity of preferences sampled during training. Extensive experiments with Stable Diffusion 1. 5 and Stable Diffusion XL confirm that our method delivers substantial gains.

PDF Details

AAAI Conference 2024 Conference Paper

Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving

Junkai Xu
Liang Peng
Haoran Cheng
Linxuan Xia
Qi Zhou
Dan Deng
Wei Qian
Wenxiao Wang

Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera setting cannot produce suitable dense 3D features due to the projection nature and uncontrollable densification process. To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering. Specifically, we employ volume rendering to process the dense 3D features to obtain corresponding 2D features (e.g., depth maps, semantic maps), which are supervised by associated labels in the training. This manner regulates the generation of dense 3D features on the feature level, providing appropriate dense and unified features for multiple perception tasks. Therefore, our approach is termed Vampire, stands for ``Volume rendering As Multi-camera Perception Intermediate feature REgulator''. Experimental results on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates fine-grained and appropriate extraction of dense 3D features, and is competitive with existing SOTA methods across diverse downstream perception tasks like 3D occupancy prediction, LiDAR segmentation and 3D objection detection, while utilizing moderate GPU resources. We provide a video demonstration in the supplementary materials and Codes are available at github.com/cskkxjk/Vampire.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Semi-supervised 3D Object Detection with PatchTeacher and PillarMix

Xiaopei Wu
Liang Peng
Liang Xie
Yuenan Hou
Binbin Lin
Xiaoshui Huang
Haifeng Liu
Deng Cai

Semi-supervised learning aims to leverage numerous unlabeled data to improve the model performance. Current semi-supervised 3D object detection methods typically use a teacher to generate pseudo labels for a student, and the quality of the pseudo labels is essential for the final performance. In this paper, we propose PatchTeacher, which focuses on partial scene 3D object detection to provide high-quality pseudo labels for the student. Specifically, we divide a complete scene into a series of patches and feed them to our PatchTeacher sequentially. PatchTeacher leverages the low memory consumption advantage of partial scene detection to process point clouds with a high-resolution voxelization, which can minimize the information loss of quantization and extract more fine-grained features. However, it is non-trivial to train a detector on fractions of the scene. Therefore, we introduce three key techniques, i.e., Patch Normalizer, Quadrant Align, and Fovea Selection, to improve the performance of PatchTeacher. Moreover, we devise PillarMix, a strong data augmentation strategy that mixes truncated pillars from different LiDAR scans to generate diverse training samples and thus help the model learn more general representation. Extensive experiments conducted on Waymo and ONCE datasets verify the effectiveness and superiority of our method and we achieve new state-of-the-art results, surpassing existing methods by a large margin. Codes are available at https://github.com/LittlePey/PTPM.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

In this paper, we propose a novel benchmark, named VastTrack, aiming to facilitate the development of general visual tracking via encompassing abundant classes and videos. VastTrack consists of a few attractive properties: (1) Vast Object Category. In particular, it covers targets from 2, 115 categories, significantly surpassing object classes of existing popular benchmarks (e. g. , GOT-10k with 563 classes and LaSOT with 70 categories). Through providing such vast object classes, we expect to learn more general object tracking. (2) Larger scale. Compared with current benchmarks, VastTrack provides 50, 610 videos with 4. 2 million frames, which makes it to date the largest dataset in term of the number of videos, and hence could benefit training even more powerful visual trackers in the deep learning era. (3) Rich Annotation. Besides conventional bounding box annotations, VastTrack also provides linguistic descriptions with more than 50K sentences for the videos. Such rich annotations of VastTrack enable the development of both vision-only and vision-language tracking. In order to ensure precise annotation, each frame in the videos is manually labeled with multi-stage of careful inspections and refinements. To understand performance of existing trackers and to provide baselines for future comparison, we extensively evaluate 25 representative trackers. The results, not surprisingly, display significant drops compared to those on current datasets due to lack of abundant categories and videos from diverse scenarios for training, and more efforts are urgently required to improve general visual tracking. Our VastTrack, the toolkit, and evaluation results are publicly available at https: //github. com/HengLan/VastTrack.

PDF Details DOI

ICRA Conference 2023 Conference Paper

Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective

Wenbo Shao
Yanchao Xu
Liang Peng
Jun Li 0082
Hong Wang

Motion prediction is essential for safe and efficient autonomous driving. However, the inexplicability and uncertainty of complex artificial intelligence models may lead to unpredictable failures of the motion prediction module, which may mislead the system to make unsafe decisions. Therefore, it is necessary to develop methods to guarantee reliable autonomous driving, where failure detection is a potential direction. Uncertainty estimates can be used to quantify the degree of confidence a model has in its predictions and may be valuable for failure detection. We propose a framework of failure detection for motion prediction from the uncertainty perspective, considering both motion uncertainty and model uncertainty, and formulate various uncertainty scores according to different prediction stages. The proposed approach is evaluated based on different motion prediction algorithms, uncertainty estimation methods, uncertainty scores, etc. , and the results show that uncertainty is promising for failure detection for motion prediction but should be used with caution.

Details

AAAI Conference 2022 Conference Paper

Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity

Jie Xu
Chao Li
Yazhou Ren
Liang Peng
Yujie Mo
Xiaoshuang Shi
Xiaofeng Zhu

Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the multi-view data containing missing data in some views. Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, (2) the quality of features after fusion might be interfered by the low-quality views, especially the inaccurate imputed views. To avoid these issues, this work presents an imputation-free and fusion-free deep IMVC framework. First, the proposed method builds a deep embedding feature learning and clustering model for each view individually. Our method then nonlinearly maps the embedding features of complete data into a high-dimensional space to discover linear separability. Concretely, this paper provides an implementation of the high-dimensional mapping as well as shows the mechanism to mine the multi-view cluster complementarity. This complementary information is then transformed to the supervised information with high confidence, aiming to achieve the multi-view clustering consistency for the complete data and incomplete data. Furthermore, we design an EM-like optimization strategy to alternately promote feature learning and clustering. Extensive experiments on real-world multi-view datasets demonstrate that our method achieves superior clustering performance over state-of-the-art methods.

PDF Details

AAAI Conference 2022 Conference Paper

Simple Unsupervised Graph Representation Learning

Yujie Mo
Liang Peng
Jie Xu
Xiaoshuang Shi
Xiaofeng Zhu

In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the interclass variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in a small generalization error, thereby obtaining an effective model. Furthermore, our method removes widely used data augmentation and discriminator from previous graph contrastive learning methods, meanwhile available to output low-dimensional embeddings, leading to an efficient model. Experimental results on various real-world datasets demonstrate the effectiveness and efficiency of our method, compared to state-of-the-art methods. The source codes are released at https: //github. com/YujieMo/SUGRL.

PDF Details

ICLR Conference 2022 Conference Paper

WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection

Liang Peng
Senbo Yan
Boxi Wu
Zheng Yang 0008
Xiaofei He 0001
Deng Cai 0001

Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection methods highly rely on training with the manually annotated 3D box labels on the LiDAR point clouds. This annotation process is very laborious and expensive. To dispense with the reliance on 3D box labels, in this paper we explore the weakly supervised monocular 3D detection. Specifically, we first detect 2D boxes on the image. Then, we adopt the generated 2D boxes to select corresponding RoI LiDAR points as the weak supervision. Eventually, we adopt a network to predict 3D boxes which can tightly align with associated RoI LiDAR points. This network is learned by minimizing our newly-proposed 3D alignment loss between the 3D box estimates and the corresponding RoI LiDAR points. We will illustrate the potential challenges of the above learning problem and resolve these challenges by introducing several effective designs into our method. Codes are available at https://github.com/SPengLiang/WeakM3D.

Details

ICRA Conference 2021 Conference Paper

Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Chen-Chen Fan
Haiqun Xie
Liang Peng
Hongjun Yang
Zhen-Liang Ni
Guan'an Wang
Yan-Jie Zhou
Sheng Chen

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89. 47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.

Details

IROS Conference 2015 Conference Paper

Design of CASIA-ARM: A novel rehabilitation robot for upper limbs

Liang Peng
Zeng-Guang Hou
Long Peng 0001
Weiqun Wang

The population of disabled stroke survivors is increasing sharply over the world, and robot-assisted training has been proved to be effective to help motor relearning and alleviate the shortage of physical therapists. This paper presents the design details of a novel upper limb rehabilitation robot named CASIA-ARM, which can assist poststroke patients to perform shoulder and elbow rehabilitation training in the horizontal plane, and provide both force and visual feedback to the patient: 1) A five-bar closed-chain structure is designed to realize a stiff and low-inertial mechanism. Workspace and singularity analysis is given, which guarantees that the workspace is large enough for upper limb moving and no singularity in the workspace. 2) Cable transmission, motor current controller and impedance control method guarantee the transparent and compliant interaction between the human and the robot. 3) Implementation examples of passive training based on position control and active training based on impedance control are presented in detail. 4) In order to evaluate the robot's applicability and performance, examples of trajectory tracking and active reaching tasks are used in this study, and the preliminary test results are also given (tracking error: 0. 86±0: 42 mm, force control error: 1. 41±0. 79 N in the X direction, and 1. 22±0. 91 N in the Y direction).

Details

ICRA Conference 2014 Conference Paper

Dynamics modeling and identification of the human-robot interface based on a lower limb rehabilitation robot

Weiqun Wang
Zeng-Guang Hou
Lina Tong
Yixiong Chen
Liang Peng
Min Tan 0001

A lower limb rehabilitation robot, namely iLeg, has been developed recently. Since active exercises have been proven to be effective for neurorehabilitation and motor recovery, they are suggested to be implemented on iLeg. To this goal, patients' motion intention should be recognized. Therefore, a method based on the dynamic model of the human-robot interface (HRI) is designed to recognize the human motion intention. This paper is devoted to modeling and identifying the dynamics of the HRI. Firstly, the dynamic model of the HRI is designed by combining the dynamic models of the human leg and iLeg, where the human leg dynamic model (HLDM) is mainly concerned. By considering the motion trajectories during the rehabilitation exercises provided by iLeg, the human leg can be taken as a manipulator with two degrees of freedom; meanwhile, the joint angles and torques of the human leg can be measured indirectly by using the position and torque sensors mounted on the joints of iLeg. As a result, an 8-parameter HLDM can be designed by using the Lagrangian method. Then, the dynamic model of the HRI is identified by respectively and independently identifying the undetermined dynamic parameters of iLeg and the HLDM, where the dynamic parameters of the HLDM are mainly considered. Finally, the feasibility of the dynamic model of the HRI is validated by experiments.

Details