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Jingyi Wang

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

Dynamic heuristic phased Double Deep Q Network path planning algorithm based on Gaussian mixture regression in discrete traffic environment

Ruixin Zhang
Qing Xu
Kai Sun
Zhilin He
Yi Liu
Youneng Su
Jingyi Wang
Xinming Zhu

In outdoor path planning, effectively balancing the relationship between path capacity and path length remains a significant challenge in current research. To address this challenge, we propose a phased DDQN (Double Deep Q Network) path planning algorithm that integrates Gaussian mixture regression and dynamic heuristics within a discrete traffic environment: GMR-DDQN (Double deep Q network with Gaussian mixture regression). First, the influence of land cover and terrain on vehicle capacity is considered, leading to the formulation of a reward function and the development of a multi-coupled discrete toll cost model. Next, toll costs are incorporated into the DDQN using a greedy strategy, while heuristic methods are applied to generate multiple demonstration trajectories with minimal training. Then, Gaussian mixture regression is employed to gather demonstration trajectory data, construct the proposed path, and generate a buffer-constrained sampling area. The model is further trained to refine the trajectories. Finally, the performance of the algorithm is evaluated in three different traffic environments. Experimental results indicate that, compared to the improved DQN (Deep Q Network) and DDQN algorithms, GMR-DDQN reduces code execution time by 50. 27 %–74. 93 %, shortens path passage time by 27. 12 %–37. 29 %, decreases the average number of steps by 78. 12 %–88. 33 %, and increases the average reward by at least 18, 000 points. Additionally, compared to the unconstrained GMR-DDQN, GMR-DDQN cuts code running time by 25. 79 %–50. 27 %. The results demonstrate that GMR-DDQN can efficiently utilize the road network to enhance traffic efficiency.

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AIIM Journal 2026 Journal Article

Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries

Shuai Wang
Tengjin Weng
Jingyi Wang
Kai Zhao
Yang Shen
Zhidong Zhao
Yixiu Liu
Pengfei Jiao

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised setting has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways; (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97. 75% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 3. 7%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.

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ICML Conference 2025 Conference Paper

An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions

Yuhan Ye
Ying Cui
Jingyi Wang

We propose an online adaptive sampling algorithm for solving stochastic nonsmooth difference-of-convex (DC) problems under time-varying distributions. At each iteration, the algorithm relies solely on data generated from the current distribution and employs distinct adaptive sampling rates for the convex and concave components of the DC function, a novel design guided by our theoretical analysis. We show that, under proper conditions on the convergence of distributions, the algorithm converges subsequentially to DC critical points almost surely. Furthermore, the sample size requirement of our proposed algorithm matches the results achieved in the smooth case or when a measurable subgradient selector is available, both under static distributions. A key element of this analysis is the derivation of a novel $O(\sqrt{p/n})$ pointwise convergence rate (modulo logarithmic factors) for the sample average approximation of subdifferential mappings, where $p$ is the dimension of the variable and $n$ is the sample size – a result of independent interest. Numerical experiments confirm that the proposed algorithm is both efficient and effective for addressing stochastic nonsmooth problems.

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NeurIPS Conference 2025 Conference Paper

Convergence Rates of Constrained Expected Improvement

Haowei Wang
Jingyi Wang
Zhongxiang Dai
Nai-Yuan Chiang
Szu Hui Ng
Cosmin Petra

Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrained expected improvement (CEI) algorithm. CEI is a natural extension of expected improvement (EI) when constraints are incorporated. However, the theoretical convergence rate of CEI has not been established. In this work, we study the convergence rate of CEI by analyzing its simple regret upper bound. First, we show that when the objective function $f$ and constraint function $c$ are assumed to each lie in a reproducing kernel Hilbert space (RKHS), CEI achieves the convergence rates of $\mathcal{O} \left(t^{-\frac{1}{2}}\log^{\frac{d+1}{2}}(t) \right) \ \text{and }\ \mathcal{O}\left(t^{\frac{-\nu}{2\nu+d}} \log^{\frac{\nu}{2\nu+d}}(t)\right)$ for the commonly used squared exponential and Matérn kernels, respectively. Second, we show that when $f$ is assumed to be sampled from Gaussian processes (GPs), CEI achieves similar convergence rates with a high probability. Numerical experiments are performed to validate the theoretical analysis.

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ICRA Conference 2025 Conference Paper

Feedback RoI Features Improve Aerial Object Detection

Botao Ren
Botian Xu
Jingyi Wang
Hanwei Gao
Qiankun Yu
Zhidong Deng

Research in visual perception has shown that the human visual system utilizes high-level feedback information to guide lower-level processing, enabling adaptation to signals of varying characteristics. Inspired by this, we propose the Feedback multi-Level feature Extractor (Flex) to dynamically adjust feature selection in object detection based on image-wise and instance-level feedback information. This is particularly beneficial for applications such as aerial object detection, UAV-based target recognition and autonomous vehicle navigation, where global image quality issues like sensor degradation, foggy, or rainy conditions can impact detection performance. Flex adapts to variations in image quality, refining the feature extraction process to improve robustness against these challenges. Experimental results demonstrate that Flex consistently enhances a range of state-of-the-art methods on challenging aerial object detection datasets, including DOTA-v1. 0, DOTA-v1. 5, and HRSC2016. Furthermore, additional experiments on MS COCO confirm the module's effectiveness in general object detection tasks. Our quantitative and qualitative analyses reveal that the improvements are strongly correlated with image quality, aligning with our original motivation to address global image quality issues in real-world scenarios.

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EAAI Journal 2024 Journal Article

Data-driven deformation prediction and control for existing tunnels below shield tunneling

Zongbao Feng
Jingyi Wang
Wen Liu
Tiejun Li
Xianguo Wu
Pengxin Zhao

In order to effectively and accurately control the existing adjacent tunnel deformation caused by shield adjacent undercrossing construction, a hybrid intelligent framework for predicting and analyzing existing tunnel deformation based on a Bayesian optimization light gradient boosting machine (BO-LGBM) and a digital twin system is established in this paper. The BO-LGBM model forecasts five current tunnel deformation control targets while identifying crucial features influencing adjacent tunnel deformation. This prediction model for existing tunnel deformation under shield tunneling is subject to a comparative assessment of five machine learning algorithms, with its interpretability analyzed utilizing the Shapley Additive exPlanations (SHAP) algorithm. Furthermore, a digital twin system is established to furnish a visual platform for making informed decisions regarding the control of existing tunnel deformations. The applicability and validity of the proposed method are tested via a case study from the Wuhan Metro. The results indicate that: (1) A digital twin system is established, the existing tunnel deformations are monitored, and construction parameters are recommended for real-time adjustment. (2) The proposed BO-LGBM framework can realize rapid and accurate deformation prediction, and the goodness of fit (R 2 ) range of the five targets is 0. 951–0. 977. (3) An early warning standard is established, corresponding treatment measures are proposed for existing tunnel deformation control, and control measures are proposed and adopted in this case. The proposed prediction and control framework for deformation of existing tunnels adjacent to shield tunneling provides a timely reference for ensuring safe tunneling operations.

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ICRA Conference 2023 Conference Paper

Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs

Jingyi Wang
Jinfa Huang
Can Zhang 0001
Zhidong Deng

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial modeling during dynamic scene graph generation, it is particularly intractable to learn time-variant relations in dynamic scene graphs among frames. In this paper, we propose a Time-variant Relation-aware TRansformer (TR 2 ), which aims to model the temporal change of relations in dynamic scene graphs. Explicitly, we leverage the difference of text embeddings of prompted sentences about relation labels as the supervision signal for relations. In this way, cross-modality feature guidance is realized for the learning of time-variant relations. Implicitly, we design a relation feature fusion module with a transformer and an additional message token that describes the difference between adjacent frames. Extensive experiments on the Action Genome dataset prove that our TR2 can effectively model the time-variant relations. TR2 significantly outperforms previous state-of-the-art methods under two different settings by 2. 1 % and 2. 6% respectively.

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JBHI Journal 2023 Journal Article

Improving the Quality of Fetal Heart Ultrasound Imaging With Multihead Enhanced Self-Attention and Contrastive Learning

Yingying Zhang
Haogang Zhu
Jian Cheng
Jingyi Wang
Xiaoyan Gu
Jiancheng Han
Ye Zhang
Ying Zhao

Fetal congenital heart disease (FCHD) is a common, serious birth defect affecting ∼1% of newborns annually. Fetal echocardiography is the most effective and important technique for prenatal FCHD diagnosis. The prerequisites for accurate ultrasound FCHD diagnosis are accurate view recognition and high-quality diagnostic view extraction. However, these manual clinical procedures have drawbacks such as, varying technical capabilities and inefficiency. Therefore, the automatic identification of high-quality multiview fetal heart scan images is highly desirable to improve prenatal diagnosis efficiency and accuracy of FCHD. Here, we present a framework for multiview fetal heart ultrasound image recognition and quality assessment that comprises two parts: a multiview classification and localization network (MCLN) and an improved contrastive learning network (ICLN). In the MCLN, a multihead enhanced self-attention mechanism is applied to construct the classification network and identify six accurate and interpretable views of the fetal heart. In the ICLN, anatomical structure standardization and image clarity are considered. With contrastive learning, the absolute loss, feature relative loss and predicted value relative loss are combined to achieve favorable quality assessment results. Experiments show that the MCLN outperforms other state-of-the-art networks by 1. 52–13. 61% when determining the F1 score in six standard view recognition tasks, and the ICLN is comparable to the performance of expert cardiologists in the quality assessment of fetal heart ultrasound images, reaching 97% on a test set within 2 points for the four-chamber view task. Thus, our architecture offers great potential in helping cardiologists improve quality control for fetal echocardiographic images in clinical practice.

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