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Ying Huang

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14 papers

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EAAI Journal 2026 Journal Article

Buckling deformation reconstruction from strain distributions via U-shaped networks and knowledge distillation

Sike Wang
Xingyu Wang
Junyi Duan
Huaixiao Yan
Ying Huang
Chengcheng Tao

This paper presents a novel framework for reconstructing structural buckling deformation from strain distribution. The reconstruction was based on U-shaped network (UNet), a convolutional neural network (CNN) that inputs the strain field of the buckling structure to predict deformation. Two neural network architectures, UNet and Nested UNet (UNet++) were trained to reconstruct buckling deformation. A knowledge distillation approach was designed to transfer features of layers from the larger teacher model (UNet++) to the smaller student model (UNet). This approach can improve the accuracy of the student model without increasing model size. To improve knowledge distillation, we replaced uniform weights for feature transformation with adaptive weights. The developed method was validated on a mixed strain-deformation dataset from the finite element analysis and distributed strain measurement, which provided a real-world implication with diverse information. The trained UNet and UNet++ achieved normalized mean absolute error (NMAE) of 2. 74% and 1. 76%, respectively. According to the training results, the best UNet model trained with the proposed knowledge distillation method achieved an NMAE of 1. 84%, demonstrating a 31. 75% improvement. A parametric study was conducted to investigate the effect of transferring weights in the proposed framework. In addition, the effect of the framework for deformation reconstruction under varying conditions was evaluated, which indicated a general improvement. This study provides a tool to advance the capability of identifying structural buckling by leveraging CNN, smart sensing, numerical modeling, and knowledge distillation, which contributes to health monitoring and anomaly detection of structures.

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AAAI Conference 2026 Conference Paper

Dual-Branch Asymmetric Discrepancy Learning Based on Fake Image Pattern-Coexistence for AI-Generated Image Detection

Chunli Song
Jie Liu
Peiyang Wang
Ying Huang
Guixuan Zhang
Zhi Zeng
Shuwu Zhang

With the rapid advancement of generative models, high-fidelity AI-generated images have become increasingly indistinguishable from real images, posing significant challenges to traditional detection methods that rely on explicit artifacts or uniform feature learning. We hypothesize that detection ambiguity originates from pattern coexistence: synthetic images simultaneously embed (a) authentic patterns inherited from real-image distributions and (b) synthetic patterns induced by generative architectures, whereas real images maintain consistent patterns. We validate this hypothesis through SHAP-based quantitative analysis, demonstrating that synthetic images inherently exhibit a dual distribution—simultaneously containing authentic patterns and synthetic traces—while real images show a unimodal distribution. Building on this insight, this paper proposes a Dual-Branch Asymmetric Discrepancy Learning (DADL) framework. The DADL leverages multi-scale feature extraction and Asymmetric Feature Discrepancy Loss to capture and amplify such pattern differences across multiple scales. Extensive experiments on three benchmarks (AIGCDetectBenchmark, GenImage, and Chameleon) show that DADL achieves state-of-the-art performance, with particular strengths in detecting high-fidelity synthetic images from diffusion models (e.g., Midjourney, SDv1.4, SDv1.5) and enhancing generalization across diverse generative paradigms. This study not only offers an effective approach for AIGI detection but also sheds light on the intrinsic properties of synthetic images, providing a new perspective for advancing AIGI forensics.

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AAAI Conference 2026 Conference Paper

From Imitation to Discrimination: Toward a Generalized Curriculum Advantage Mechanism Enhancing Cross-Domain Reasoning Tasks

Changpeng Yang
Jinyang Wu
Yuchen Liu
Shuai Zhang
Yang Li
Qiliang Liang
Hongzhen Wang
Shuai Nie

Reinforcement learning has emerged as a paradigm for post-training large language models, boosting their reasoning capabilities. Such approaches compute an advantage value for each sample, reflecting better or worse performance than expected, thereby yielding both positive and negative signals for training. However, existing approaches often mix them indiscriminately, especially in the early stages, leading to ambiguous guidance and limited gains. To address this issue, we propose CAPO (Curriculum Advantage Policy Optimization), an adaptive curriculum mechanism based on advantage signals. The proposed mechanism bootstraps imitation learning with positive-only advantage samples to establish robust foundations, and subsequently introduces negative signals to cultivate discriminative capabilities, thereby improving generalization across complex scenarios. Compatible with diverse optimization methods including GRPO, PPO, RLOO, and Reinforce++, our method consistently achieves stable and significant improvements in mathematical reasoning tasks, and further generalizes effectively to multimodal Graphical User Interface (GUI) reasoning scenarios, establishing itself as a versatile and robust optimization paradigm.

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JBHI Journal 2025 Journal Article

A Dual-Branch Cross-Modality-Attention Network for Thyroid Nodule Diagnosis Based on Ultrasound Images and Contrast-Enhanced Ultrasound Videos

Jianning Chi
Jia-hui Chen
Bo Wu
Jin Zhao
Kai Wang
Xiaosheng Yu
Wenjun Zhang
Ying Huang

Contrast-enhanced ultrasound (CEUS) has been extensively employed as an imaging modality in thyroid nodule diagnosis due to its capacity to visualise the distribution and circulation of micro-vessels in organs and lesions in a non-invasive manner. However, current CEUS-based thyroid nodule diagnosis methods suffered from: 1) the blurred spatial boundaries between nodules and other anatomies in CEUS videos, and 2) the insufficient representations of the local structural information of nodule tissues by the features extracted only from CEUS videos. In this paper, we propose a novel dual-branch network with a cross-modality-attention mechanism for thyroid nodule diagnosis by integrating the information from tow related modalities, i. e. , CEUS videos and ultrasound image. The mechanism has two parts: US-attention-from-CEUS transformer (UAC-T) and CEUS-attention-from-US transformer (CAU-T). As such, this network imitates the manner of human radiologists by decomposing the diagnosis into two correlated tasks: 1) the spatio-temporal features extracted from CEUS are hierarchically embedded into the spatial features extracted from US with UAC-T for the nodule segmentation; 2) the US spatial features are used to guide the extraction of the CEUS spatio-temporal features with CAU-T for the nodule classification. The two tasks are intertwined in the dual-branch end-to-end network and optimized with the multi-task learning (MTL) strategy. The proposed method is evaluated on our collected thyroid US-CEUS dataset. Experimental results show that our method achieves the classification accuracy of 86. 92%, specificity of 66. 41%, and sensitivity of 97. 01%, outperforming the state-of-the-art methods. As a general contribution in the field of multi-modality diagnosis of diseases, the proposed method has provided an effective way to combine static information with its related dynamic information, improving the quality of deep learning based diagnosis with an additional benefit of explainability.

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NeurIPS Conference 2025 Conference Paper

In the field of AI-driven human-GUI interaction automation, while rapid advances in multimodal large language models and reinforcement fine-tuning techniques have yielded remarkable progress, a fundamental challenge persists: their interaction logic significantly deviates from natural human-GUI communication patterns. To address this gap, we propose Blink–Think–Link (BTL), a brain-inspired framework for human-GUI interaction that mimics the human cognitive process between users and graphical interfaces. The system decomposes interactions into three biologically plausible phases: (1) \textbf{Blink} - rapid detection and attention to relevant screen areas, analogous to saccadic eye movements; (2) \textbf{Think} - higher-level reasoning and decision-making, mirroring cognitive planning; and (3) \textbf{Link} - generation of executable commands for precise motor control, emulating human action selection mechanisms. Additionally, we introduce two key technical innovations for BTL framework: (1) Blink Data Generation - an automated annotation pipeline specifically optimized for blink data, and (2) {BTL Reward – the first rule-based reward mechanism that enables reinforcement learning driven by both process and outcome. } Building upon this framework, we develop a GUI agent model named BTL-UI, which demonstrates competitive performance across both static GUI understanding and dynamic interaction tasks in comprehensive benchmarks. These results provide conclusive empirical validation of the framework's efficacy in developing advanced GUI agents.

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JBHI Journal 2025 Journal Article

Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency

Jianning Chi
Geng Lin
Zelan Li
Wenjun Zhang
Jia-hui Chen
Ying Huang

Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasound images, resulting in segmentation over-fitting to weakly annotated regions without precise delineation of target boundaries. We propose a dual-branch weakly-supervised learning framework to optimize the backbone segmentation network by calibrating semantic features into rational spatial distribution under the indirect, coarse guidance of the bounding box mask. Specifically, in the spatial arrangement consistency branch, the maximum activations sampled from the preliminary segmentation prediction and the bounding box mask along the horizontal and vertical dimensions are compared to measure the rationality of the approximate target localization. In the hierarchical prediction consistency branch, the target and background prototypes are encapsulated from the semantic features under the combined guidance of the preliminary segmentation prediction and the bounding box mask. The secondary segmentation prediction induced from the prototypes is compared with the preliminary prediction to quantify the rationality of the elaborated target and background semantic feature perception. Experiments on three thyroid datasets illustrate that our model outperforms existing weakly-supervised methods for thyroid gland and nodule segmentation and is comparable to the performance of fully-supervised methods with reduced annotation time. The proposed method has provided a weakly-supervised segmentation strategy by simultaneously considering the target's location and the rationality of target and background semantic features distribution. It can improve the applicability of deep learning based segmentation in the clinical practice.

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EAAI Journal 2025 Journal Article

Comprehensive performance evaluation of valuable medical equipment based on cloud modelling and combined weighting methodologies

Xingtong Zhang
Saifeng Fang
Yongchun Jin
Ying Huang
Shucheng Wang
Jie Wang
Yunhua Xu

The construction of the performance evaluation index system for valuable medical equipment is the basis for measuring the use of medical equipment in hospitals. It is crucial to the management and evaluation of equipment. This study aimed to develop a comprehensive evaluation model that thoroughly assesses the operational status, utilization efficiency, and service quality of valuable medical equipment in hospitals. The performance evaluation index system for valuable medical equipment was constructed using four dimensions. The subjective weights were determined using the Analytical Hierarchy Process (AHP), while the objective weights were calculated using the Entropy Weight Method (EWM). The combined weights of the index system were derived by integrating both subjective and objective weights through game theory. The Delphi method was employed to establish a standard cloud model, which was subsequently integrated with the combined weights to construct a comprehensive evaluation cloud model. This research evaluated the performance of nine newly acquired pieces of valuable medical equipment that were operational in a hospital after 2020, thereby validating the reliability of the proposed model. The outcomes indicate that the model effectively addresses the problem of data uncertainty in fuzzy evaluations while alleviating the limitations associated with single weighting methods. The performance evaluation model for medical equipment proposed in this study provided an innovative and effective strategy for assessing valuable medical equipment in hospitals, thereby enhancing the scientific and effective management of medical equipment.

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EAAI Journal 2025 Journal Article

Enhanced facial image essence transfer via semantic guidance

Ailin Li
Anyong Qin
Ying Huang

Generating novel facial images guided by given references (e. g. , face style transfer) has attracted significant attention in recent years. However, existing approaches often struggle to achieve a balance between transfer quality and identity preservation. In this paper, we propose a novel method for reference-guided facial image essence transfer, which aims to effectively extract and transfer high-level semantic features (“essence”) from a single reference image to a source image without compromising identity preservation. Specifically, we leverage Contrastive Language-Image Pre-training (CLIP) to address this task from an image-driven perspective, exploiting CLIP’s rich understanding of diverse visual concepts. We introduce a novel essence contrastive loss designed to utilize semantic information captured from different image perspectives within the CLIP embedding space, which effectively guides the essence transfer in a semantically meaningful direction. Furthermore, to enhance feature fusion and ensure precise attribute transfer, we propose an Essence-Preserved Modulation (EPM) module that explicitly maps the CLIP embeddings of the reference image into the latent space of the generative model. Additionally, an essence degree indicator is introduced to allow flexible control over the extent of essence transferred to the generated images, significantly improving the practical applicability of our method. Extensive experiments demonstrate that our approach consistently generates realistic facial images that effectively capture diverse essence attributes from various references while simultaneously achieving superior facial identity preservation compared to existing methods.

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EAAI Journal 2025 Journal Article

From smoke to fire: A forest fire early warning and risk assessment model fusing multimodal data

Peixian Jin
Pengle Cheng
Xiaodong Liu
Ying Huang

Experienced forest firefighters can integrate knowledge of smoke patterns and risk factors to assess fire risk and anticipate potential fire spread and outbreaks in complex, variable environments. This study simulates the previously mentioned monitoring process and presents the Multimodal Smoke Risk Estimation Network (MM-SRENet), an innovative multimodal fusion model. This model uniquely integrates the Multi-Scale Dilation Block and Repulsion Loss into a lightweight and efficient target detection system to accurately identify smoke's presence. Furthermore, an advanced backbone based on star operation extracts the scene characteristics associated with smoke and merges them with various fire risk factors. The objective is to simulate fire risk assessments in smoke scenarios and to reduce the misallocation of fire resources resulting from false alarms and missed alerts. The proposed model was trained and validated on a multimodal dataset comprising multiple backgrounds. It successfully identified smoke features and fire potential risks in different scenarios, achieving a prediction accuracy of 93. 06 %. Fusing smoke images with fire risk data resulted in an 18. 75 % improvement in recognition accuracy compared to the single modal model. This work bridges the gap between multimodal data fusion and forest fire risk monitoring, providing a new direction for future intelligent forest fire prevention and control practices.

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EAAI Journal 2025 Journal Article

HK-MOEA/D: A historical knowledge-guided resource allocation for decomposition multiobjective optimization

Wei Li
Xiaolong Zeng
Ying Huang
Yiu-ming Cheung

Decomposition-based multiobjective evolutionary algorithms is one of the prevailing algorithmic frameworks for multiobjective optimization. This framework distributes the same amount of evolutionary computing resources to each subproblems, but it ignores the variable contributions of different subproblems to population during the evolution. Resource allocation strategies (RAs) have been proposed to dynamically allocate appropriate evolutionary computational resources to different subproblems, with the aim of addressing this limitation. However, the majority of RA strategies result in inefficiencies and mistakes when performing subproblem assessment, thus generating unsuitable algorithmic results. To address this problem, this paper proposes a decomposition-based multiobjective evolutionary algorithm (HK-MOEA/D). The HK-MOEA/D algorithm uses a historical knowledge-guided RA strategy to evaluate the subproblem’s evolvability, allocate evolutionary computational resources based on the evaluation value, and adaptively select genetic operators based on the evaluation value to either help the subproblem converge or move away from a local optimum. Additionally, the density-first individual selection mechanism of the external archive is utilized to improve the diversity of the algorithm. An external archive update mechanism based on θ -dominance is also used to store solutions that are truly worth keeping to guide the evaluation of subproblem evolvability. The efficacy of the proposed algorithm is evaluated by comparing it with seven state-of-the-art algorithms on three types of benchmark functions and three types of real-world application problems. The experimental results show that HK-MOEA/D accurately evaluates the evolvability of the subproblems and displays reliable performance in a variety of complex Pareto front optimization problems.

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EAAI Journal 2025 Journal Article

Super-resolution reconstruction of WorldView-3 multispectral satellite images based on generative adversarial networks

Mingqiang Guo
Hanbin Huang
Ran Liu
Chenglong Shao
Ying Huang

Due to the limitations of optical sensors, satellites often sacrifice spatial resolution to obtain higher spectral resolution. Higher-resolution multispectral images contribute to accurate land feature identification, change monitoring, and environmental analysis. However, satellite remote sensing images encompass diverse terrains and surface features, which causes existing models for image reconstruction to frequently encounter challenges such as loss of high-frequency texture details and artifacts. To tackle this issue, this paper proposes a super-resolution method based on generative adversarial networks to enhance the spatial resolution of WorldView-3 satellite images from 1. 2 m to 0. 3 m across 8 spectral bands. Firstly, we introduce coordinate attention mechanisms in the generator to enhance the network’s ability to reconstruct spatial high-frequency details. In the discriminator, spectral normalization layers are integrated to enhance the stability of the adversarial network. Furthermore, a joint loss function is devised, comprising Charbonnier loss, spectral loss, and adversarial loss weighting. The Charbonnier loss effectively handles outliers, thereby mitigating artifact generation, while the spectral loss enriches the network’s spectral extraction capability, preventing spectral distortion in multispectral images. The adversarial loss incentivizes the generator to generate realistic images. The proposed method is evaluated using real WorldView-3 multispectral satellite images. Experimental results on two datasets demonstrate that the proposed method not only preserves more accurate high-frequency texture details and effectively reduces artifacts, but also maintains greater spectral fidelity than state-of-the-art approaches. It achieves optimal Learned Perceptual Image Patch Similarity (LPIPS) and Spectral Angle Mapper (SAM) scores, with improvements of 0. 005 and 0. 014 over the second-best method, respectively.

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YNIMG Journal 2023 Journal Article

Genetic Influence on Gyral Peaks

Ying Huang
Tuo Zhang
Songyao Zhang
Weihan Zhang
Li Yang
Dajiang Zhu
Tianming Liu
Xi Jiang

Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

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YNIMG Journal 2022 Journal Article

Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age

Tao Zhong
Jingkuan Wei
Kunhua Wu
Liangjun Chen
Fenqiang Zhao
Yuchen Pei
Ya Wang
Hongjiang Zhang

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.

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ICML Conference 2020 Conference Paper

More Information Supervised Probabilistic Deep Face Embedding Learning

Ying Huang
Shangfeng Qiu
Wenwei Zhang
Xianghui Luo
Jinzhuo Wang

Researches using margin based comparison loss demonstrate the effectiveness of penalizing the distance between face feature and their corresponding class centers. Despite their popularity and excellent performance, they do not explicitly encourage the generic embedding learning for an open set recognition problem. In this paper, we analyse margin based softmax loss in probability view. With this perspective, we propose two general principles: 1) monotonically decreasing and 2) margin probability penalty, for designing new margin loss functions. Unlike methods optimized with single comparison metric, we provide a new perspective to treat open set face recognition as a problem of information transmission. And the generalization capability for face embedding is gained with more clean information. An auto-encoder architecture called Linear-Auto-TS-Encoder(LATSE) is proposed to corroborate this finding. Extensive experiments on several benchmarks demonstrate that LATSE help face embedding to gain more generalization capability and it boost the single model performance with open training dataset to more than 99% on MegaFace test.

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