Author name cluster

Yu Jiang

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17 papers

2 author rows

EAAI Journal 2026 Journal Article

A self-supervised visual auto-regressive framework for medical hyperspectral anomaly detection

Lingqin Chen
Jinzhuang Xu
Yu Jiang
Zhihao Zhan
Xiaoli Yang
Mingzhong Pan
Chenglong Zhang
Xuesen Xu

Medical hyperspectral imaging non-invasively acquires tissue spatial–spectral information, holding promise for early diagnosis, real-time and precision treatment. However, complex tissue properties and dependence on labeled data challenge lesion identification. Hyperspectral anomaly detection, widely applied for its unsupervised capability and sensitivity to anomalies, is promising to identify unknown lesions without requiring labels. Nevertheless, current hyperspectral anomaly detection methods often underperform in medical hyperspectral images due to misalignment with biological tissue structures, inadequate use of spectral features, and noise sensitivity. Therefore, we propose a self-supervised framework based on visual auto-regressive modeling for medical hyperspectral anomaly detection. We introduce the multi-scale generation paradigm of visual auto-regressive modeling for the first time to accommodate the structure of biological tissues. Second, we employ the graph convolutional network to optimize spectral features. A multi-scale fusion module improves feature integration and robustness. Finally, enhanced separated training refines the reconstruction with spatial context. Our method effectively leverages spectral–spatial features, enabling robust background reconstruction and accurate anomaly detection. Experiments on medical hyperspectral imaging datasets show that it outperforms state-of-the-art methods, with strong noise robustness and generalization, which demonstrating high clinical potential.

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AAAI Conference 2026 Conference Paper

HumanPro: Single-view 3D Clothed Human Reconstruction with Progressive Normal Guidance

Jianchi Sun
Fei Luo
Wenzhuo Fan
Yu Jiang
Chunxia Xiao

Reconstructing fine-grained geometry of clothed human from single-view image is a challenging task, particularly in accurately recovering complex shapes and generating clothes details. To address these limitations, we propose a novel approach named HumanPro, which estimates high-quality human normals via a generative model, and progressively deforms a parametric body into the final clothed human mesh guided by normals. First, we propose a geometry-aware latent diffusion model with a normal enhancer to estimate high-quality human normals from four views. Then, we propose a progressive mesh optimization consisting of shape-aware deformation alignment and global-to-patch detail refinement for human mesh reconstruction. The shape-aware deformation alignment applies image morphing to learn the shape-level gap of normals, addressing large-scale deformation of complex clothes. It can recover the overall silhouette of a clothed human, and serves as an initialization for the global-to-patch detail refinement. Our detail refinement combines global and patch-wise optimization strategies to iteratively produce the clothed human mesh by minimizing the pixel-level difference of normals. This way effectively recovers fine-grained details while avoiding local minima. Extensive experiments demonstrate that HumanPro can deal with various challenging scenarios and outperforms state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

HyperSign: Hierarchical Hypergraph-based Co-occurrence Modeling for Sign Language Recognition and Translation

Qianren Guo
Yuehang Wang
Yongji Zhang
Qi Chu
Sen Liu
Yu Jiang

Effectively capturing co-occurrence signals, such as hand shapes, facial expressions, and body postures, is critical for semantic understanding in sign language recognition (SLR) and translation (SLT). Although skeleton data offer greater efficiency and robustness than RGB inputs, existing methods typically rely on pairwise graph structures, limiting their ability to model complex high-order interactions across body regions. To address this limitation, we propose HyperSign, a hierarchical hypergraph neural network that systematically captures high-order co-occurrence patterns among diverse body parts. The Co-occurrence Graph Perception Module jointly learns relational structures via three complementary pathways: (1) traditional graph convolutions for modeling physical joint connections, (2) dynamic geometric hypergraphs constructed via k-nearest neighbors to encode local spatial patterns, and (3) soft hypergraphs generated by learnable prototypes to reveal latent semantic associations. To further enhance structural modeling and semantic consistency, a Meta-Part Hypergraph Fusion Module abstracts feature streams from the hands, face, and body into unified hypergraph nodes, while leveraging empirically derived co-occurrence priors to model high-order cross-part dependencies. Moreover, an uncertainty-aware collaborative distillation mechanism guides the model to focus on critical body regions. Extensive experiments on standard SLR and SLT benchmarks (e.g., PHOENIX-2014, PHOENIX-2014T, and CSL-Daily) demonstrate that HyperSign not only outperforms existing skeleton-based approaches in both speed and accuracy but also achieves competitive or superior results compared to several state-of-the-art RGB-based methods across multiple evaluation metrics.

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IROS Conference 2025 Conference Paper

ArtGS: 3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects

Qiaojun Yu
Xibin Yuan
Yu Jiang
Junting Chen
Dongzhe Zheng
Ce Hao
Yang You 0004
Yixing Chen 0008

Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this work, we introduce ArtGS, a novel framework that extends 3D Gaussian Splatting (3DGS) by integrating visual-physical modeling for articulated object understanding and interaction. ArtGS begins with multi-view RGB-D reconstruction, followed by reasoning with a vision-language model (VLM) to extract semantic and structural information, particularly the articulated bones. Through dynamic, differentiable 3DGS-based rendering, ArtGS optimizes the parameters of the articulated bones, ensuring physically consistent motion constraints and enhancing the manipulation policy. By leveraging dynamic Gaussian splatting, cross-embodiment adaptability, and closed-loop optimization, ArtGS establishes a new framework for efficient, scalable, and generalizable articulated object modeling and manipulation. Experiments conducted in both simulation and real-world environments demonstrate that ArtGS significantly outperforms previous methods in joint estimation accuracy and manipulation success rates across a variety of articulated objects. Additional images and videos are available on the project website: sites.google.com/view/artgs.

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ICRA Conference 2025 Conference Paper

Joint 3D Point Cloud Segmentation Using Real-Sim Loop: From Panels to Trees and Branches

Tian Qiu
Ruiming Du
Nikolai Spine
Lailiang Cheng
Yu Jiang

Modern orchards are planted in structured rows with distinct panel divisions to improve management. Accurate and efficient joint segmentation of point cloud from Panel to Tree and Branch (P2TB) is essential for robotic operations. However, most current segmentation methods focus on single-instance segmentation and depend on a sequence of deep networks to perform joint tasks. This strategy hinders the use of hierarchical information embedded in the data, leading to both error accumulation and increased costs for annotation and computation, which limits its scalability for real-world applications. In this study, we proposed a novel approach that incorporated a Real2Sim L-TreeGen for training data generation and a joint model (J-P2TB) designed for the P2TB task. The J-P2TB model, trained on the generated simulation dataset, was used for joint segmentation of real-world panel point clouds via zero-shot learning. Compared to representative methods, our model outperformed them in most segmentation metrics while using 40% fewer learnable parameters. This Sim2Real result highlighted the efficacy of L-TreeGen in model training and the performance of J-P2TB for joint segmentation, demonstrating its strong accuracy, efficiency, and generalizability for real-world applications. These improvements would not only greatly benefit the development of robots for automated orchard operations but also advance digital twin technology, enabling the facilitation of field robotics across various domains.

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EAAI Journal 2025 Journal Article

Sample-efficient planning-control framework for autonomous underwater vehicle docking using lightweight Cross Q-Learning

Kaixin Zhang
Yuelong Xu
Minghao Zhao
Yu Jiang

Autonomous Underwater Vehicles (AUVs) are essential for long-duration missions in underwater sensor networks, but their operation is constrained by limited onboard energy and low-bandwidth communication. Docking stations are deployed to support energy replenishment and data transfer, requiring AUVs to autonomously plan return trajectories and achieve precise docking. Traditional motion planning and control methods for AUV docking often decouple global motion planning from local control or rely on accurate dynamics models, while existing deep reinforcement learning (DRL) approaches frequently neglect sample efficiency. This work presents a novel DRL framework that tightly integrates motion planning and control while emphasizing sample efficiency to enable reliable and efficient AUV docking. The docking task is formulated as an Markov Decision Process (MDP), in which a structured reward function is defined to guide the agent towards goal-directed behavior in an interpretable and sample-efficient manner. A lightweight Cross Q-Learning (CrossQ-Lite) algorithm is developed by removing target networks and incorporating batch normalization, achieving stable learning with approximately 92. 8% fewer parameters while maintaining comparable performance. Simulation results demonstrate that, under the proposed MDP formulation, CrossQ-Lite achieves a minimum of 1. 39 × higher sample efficiency and improved training stability compared to state-of-the-art DRL baselines. The trained model reliably completes AUV docking tasks, providing a practical and high-performance solution for autonomous underwater sensor networks.

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IROS Conference 2024 Conference Paper

(Real2Sim) -1: 3D Branch Point Cloud Completion for Robotic Pruning in Apple Orchards

Tian Qiu
Alan Zoubi
Nikolai Spine
Lailiang Cheng
Yu Jiang

Robotic branch pruning, a rapidly growing field addressing labor shortages in agriculture, requires detailed perception of branch geometry and topology. However, point clouds obtained in agricultural settings often lack completeness, limiting pruning accuracy. This work addressed point cloud quality via a closed-loop approach, (Real2Sim) −1. Leveraging a Real-to-Simulation (Real2Sim) data generation pipeline, we generated simulated 3D apple trees based on realistically characterized apple tree information without manual parameterization. These 3D trees were used to train a simulation-based deep model that jointly performs point cloud completion and skeletonization on real-world partial branches, without extra real-world training. The Sim2Real qualitative results showed the model’s remarkable capability for geometry reconstruction and topology prediction. Additionally, we quantitatively evaluated the Sim2Real performance by comparing branch-level trait characterization errors using raw incomplete data and the best complete data. The Mean Absolute Error (MAE) reduced by 75% and 8% for branch diameter and branch angle estimation, respectively, which indicates the effectiveness of the Real2Sim data in a zero-shot generalization setting. The characterization improvements contributed to the precision and efficacy of robotic branch pruning.

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AIIM Journal 2024 Journal Article

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis

Zichen Ye
Daqian Zhang
Yuankai Zhao
Mingyang Chen
Huike Wang
Samuel Seery
Yimin Qu
Peng Xue

Background Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. Objectives To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. Methods Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. Results 2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77–86), SPE was 87 % (range 84–90), with an AUC of 0. 92 (range 0. 89–0. 94). Human-machine comparison had pooled AUCs of 0. 87 (0. 84–0. 90) and 0. 83 (0. 79–0. 86) for DL and dermatologists, respectively. Pooled AUCs were 0. 90 (0. 87–0. 93), 0. 80 (0. 76–0. 83), and 0. 88 (0. 85–0. 91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0. 88 (0. 85–0. 91) for DL, 0. 76 (0. 72–0. 79) for unassisted, and 0. 87 (0. 84–0. 90) for DL-assisted dermatologists. Conclusions Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.

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AAAI Conference 2023 Short Paper

Adaptive Constraint Partition Based Optimization Framework for Large-Scale Integer Linear Programming (Student Abstract)

Huigen Ye
Hongyan Wang
Hua Xu
Chengming Wang
Yu Jiang

Integer programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs. To solve this type of IPs, Large neighborhood search (LNS) uses an initial feasible solution and iteratively improves it by searching a large neighborhood around the current solution. However, LNS easily steps into local optima and ignores the correlation between variables to be optimized, leading to compromised performance. This paper presents a general adaptive constraint partition-based optimization framework (ACP) for large-scale IPs that can efficiently use any existing optimization solver as a subroutine. Specifically, ACP first randomly partitions the constraints into blocks, where the number of blocks is adaptively adjusted to avoid local optima. Then, ACP uses a subroutine solver to optimize the decision variables in a randomly selected block of constraints to enhance the variable correlation. ACP is compared with LNS framework with different subroutine solvers on four IPs and a real-world IP. The experimental results demonstrate that in specified wall-clock time ACP shows better performance than SCIP and Gurobi.

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ICML Conference 2023 Conference Paper

GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming

Huigen Ye
Hua Xu 0003
Hongyan Wang
Chengming Wang
Yu Jiang

The latest two-stage optimization framework based on graph neural network (GNN) and large neighborhood search (LNS) is the most popular framework in solving large-scale integer programs (IPs). However, the framework can not effectively use the embedding spatial information in GNN and still highly relies on large-scale solvers in LNS, resulting in the scale of IP being limited by the ability of the current solver and performance bottlenecks. To handle these issues, this paper presents a GNN&GBDT-guided fast optimizing framework for large-scale IPs that only uses a small-scale optimizer to solve large-scale IPs efficiently. Specifically, the proposed framework can be divided into three stages: Multi-task GNN Embedding to generate the embedding space, GBDT Prediction to effectively use the embedding spatial information, and Neighborhood Optimization to solve large-scale problems fast using the small-scale optimizer. Extensive experiments show that the proposed framework can solve IPs with millions of scales and surpass SCIP and Gurobi in the specified wall-clock time using only a small-scale optimizer with 30% of the problem size. It also shows that the proposed framework can save 99% of running time in achieving the same solution quality as SCIP, which verifies the effectiveness and efficiency of the proposed framework in solving large-scale IPs.

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AAAI Conference 2023 Short Paper

Self-Paced Learning Based Graph Convolutional Neural Network for Mixed Integer Programming (Student Abstract)

Li Chen
Hua Xu
Ziteng Wang
Chengming Wang
Yu Jiang

Graph convolutional neural network (GCN) based methods have achieved noticeable performance in solving mixed integer programming problems (MIPs). However, the generalization of existing work is limited due to the problem structure. This paper proposes a self-paced learning (SPL) based GCN network (SPGCN) with curriculum learning (CL) to make the utmost of samples. SPGCN employs a GCN model to imitate the branching variable selection during the branch and bound process, while the training process is conducted in a self-paced fashion. Specifically, SPGCN contains a loss-based automatic difficulty measurer, where the training loss of the sample represents the difficulty level. In each iteration, a dynamic training dataset is constructed according to the difficulty level for GCN model training. Experiments on four NP-hard datasets verify that CL can lead to generalization improvement and convergence speedup in solving MIPs, where SPL performs better than predefined CL methods.

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IROS Conference 2023 Conference Paper

Vision-Based Vineyard Navigation Solution with Automatic Annotation

Ertai Liu
Josephine Monica
Kaitlin M. Gold
Lance Cadle-Davidson
David Combs
Yu Jiang

Autonomous navigation is crucial for achieving the full automation of agricultural research and production management using agricultural robots. In this paper, we present a vision-based autonomous navigation approach for agriculture robots in trellised cropping systems, which stands out for its remarkable performance achieved entirely without human annotation. We propose a novel learning-based method that directly estimates the path traversibility heatmap from an RGB-D image and subsequently converts it into a preferred traversal path. One key advantage of our approach lies in its capability to predict the robot's preferred path directly, allowing us to obtain training labels without manual annotation. Specifically, we propose an automatic annotation pipeline that leverages the robot's path recorded during data collection. Furthermore, we develop a full navigation framework by integrating our path detection model with row switching modules, enabling the robot to smoothly transition between crop rows within the vineyard. We conduct extensive field trials in three different vineyards to validate the performance of our autonomous navigation framework. The results demonstrate that our approach provides a cost-effective, accurate, and robust solution for vineyard navigation.

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IROS Conference 2022 Conference Paper

An Adaptive Approach to Whole-Body Balance Control of Wheel-Bipedal Robot Ollie

Jingfan Zhang
Shuai Wang 0007
Haitao Wang
Jie Lai
Zhenshan Bing
Yu Jiang
Yu Zheng 0001
Zhengyou Zhang

The wheel-bipedal robot has the advantages of both wheeled robots and legged robots, but as a cost, it is more challenging to perform flexible movements in various surroundings while keeping it balanced. The inaccurate dynamics of the robot makes the balance problem even more intractable. To solve this problem, the robot Ollie is used as a testbed. The whole-body control (WBC) framework is adopted to enhance the dexterity of the robot with multiple degrees of freedom in the task space. Moreover, a learning-based adaptive technique is applied to assist the WBC such that the balance controller can be designed in the absence of the accurate dynamics. Physical experiments demonstrate that the robot can manage various actions, with the help of the combination of the WBC and the learning-based adaptive technique.

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IROS Conference 2022 Conference Paper

Near Real-Time Vineyard Downy Mildew Detection and Severity Estimation

Ertai Liu
Kaitlin M. Gold
Lance Cadle-Davidson
David Combs
Yu Jiang

The global grape and wine industry has been considerably impacted by diseases such as downy mildew (DM). Agricultural robots have demonstrated great potential to accurately and rapidly map DM infection for precision applications. Although the robots can autonomously acquire high-resolution images in the vineyard, data processing is mostly performed offline because of network infrastructure and onboard computing power constraints, limiting the use of agricultural robots for field operations. To address this issue, we developed a semantic segmentation model based on the modified DeepLabv3 network for near real time DM segmentation in high resolution images. Compared with state-of-the-art real time semantic segmentation models, the developed one achieved the best efficiency-accuracy balance on the DM dataset using embedded computing devices that can be easily integrated with commercial robotic platforms. DM severity estimation pipeline based on the model also showed a comparable measurement accuracy and statistical power in differentiation of fungicide treatments as the one based on offline semantic segmentation models. This enables the use of robotic perception systems for field operations.

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NeurIPS Conference 2020 Conference Paper

Joint Policy Search for Multi-agent Collaboration with Imperfect Information

Yuandong Tian
Qucheng Gong
Yu Jiang

To learn good joint policies for multi-agent collaboration with incomplete information remains a fundamental challenge. While for two-player zero-sum games, coordinate-ascent approaches (optimizing one agent's policy at a time, e. g. , self-play) work with guarantees, in multi-agent cooperative setting they often converge to sub-optimal Nash equilibrium. On the other hand, directly modeling joint policy changes in incomplete information game is nontrivial due to complicated interplay of policies (e. g. , upstream updates affect downstream state reachability). In this paper, we show global changes of game values can be decomposed to policy changes localized at each information set, with a novel term named \emph{policy-change density}. Based on this, we propose \emph{Joint Policy Search} (JPS) that iteratively improves joint policies of collaborative agents in incomplete information games, without re-evaluating the entire game. On multiple collaborative tabular games, JPS is proven to never worsen performance and can improve solutions provided by unilateral approaches (e. g, CFR), outperforming algorithms designed for collaborative policy learning (e. g. BAD). Furthermore, for real-world game whose states are too many to enumerate, \ours{} has an online form that naturally links with gradient updates. We test it to Contract Bridge, a 4-player imperfect-information game where a team of $2$ collaborates to compete against the other. In its bidding phase, players bid in turn to find a good contract through a limited information channel. Based on a strong baseline agent that bids competitive bridge purely through domain-agnostic self-play, JPS improves collaboration of team players and outperforms WBridge5, a championship-winning software, by $+0. 63$ IMPs (International Matching Points) per board over $1000$ games, substantially better than previous SoTA ($+0. 41$ IMPs/b against WBridge5). Note that $+0. 1$ IMPs/b is regarded as a nontrivial improvement in Computer Bridge.

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IJCAI Conference 2018 Conference Paper

Iterative Metric Learning for Imbalance Data Classification

Nan Wang
Xibin Zhao
Yu Jiang
Yue Gao

In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i. e. , the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.

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EAAI Journal 2014 Journal Article

A hybrid WA–CPSO-LSSVR model for dissolved oxygen content prediction in crab culture

Shuangyin Liu
Longqin Xu
Yu Jiang
Daoliang Li
Yingyi Chen
Zhenbo Li

To increase prediction accuracy, reduce aquaculture risks and optimize water quality management in intensive aquaculture ponds, this paper proposes a hybrid dissolved oxygen content forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In the modeling process, the original dissolved oxygen sequences were de-noised and decomposed into several resolution frequency signal subsets using the wavelet analysis method. Independent prediction models were developed using decomposed signals with wavelet analysis and least squares support vector regression. The independent prediction values were reconstructed to obtain the ultimate prediction results. In addition, because the kernel parameter δ and the regularization parameter γ in the LSSVR training procedure significantly influence forecasting accuracy, the Cauchy particle swarm optimization (CPSO) algorithm was used to select optimum parameter combinations for LSSVR. The proposed hybrid model was applied to predict dissolved oxygen in river crab culture ponds. Compared with traditional models, the test results of the hybrid WA–CPSO-LSSVR model demonstrate that de-noising and capturing non-stationary characteristics of dissolved oxygen signals after WA comprise a very powerful and reliable method for predicting dissolved oxygen content in intensive aquaculture accurately and quickly.

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