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Huaming Chen

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Towards Zero-Shot Diabetic Retinopathy Grading: Learning Generalized Knowledge via Prompt-Driven Matching and Emulating

Huan Wang
Haoran Li
Yuxin Lin
Huaming Chen
Jun Yan
Lijuan Wang
Jiahua Shi
Qihao Xu

As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fundus images can significantly enhance DR grading performance by expanding the field of view (FOV). However, there is a long-tailed distribution problem in fundus image analysis, i.e., a high prevalence of mild DR grades and a low prevalence of rare ones (e.g., cases of high severity), which presents a significant challenge to developing a unified model capable of detecting rare or unseen DR grades not encountered during training. In this paper, we propose ProME-DR, a Prompt-driven zero-shot DR grading framework, which leverages prompt Matching and Emulating to recognize the unseen DR categories and views beyond the training set. ProME-DR disentangles the training process into two stages to learn generalized knowledge for novel DR disease grading. Initially, ProME-DR leverages two sets of prompt units to capture semantic and inter-view consistency knowledge via a split-and-mask manner, gathering instance-level DR visual clues. Subsequently, it constructs a concept-aware emulator to generate context prompt units, linking extensible knowledge learned from the previously seen DR attributes for zero-shot DR grading. Extensive experiments conducted on eight datasets and various scenarios confirm the superiority of ProME-DR.

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UAI Conference 2025 Conference Paper

Improving Adversarial Transferability via Decision Boundary Adaptation

Jiayu Zhang 0001
Zhiyu Zhu
Zhibo Jin
Xinyi Wang 0005
Huaming Chen
Kim-Kwang Raymond Choo

Black-box attacks play a pivotal role in adversarial attacks. However, existing approaches often focus predominantly on attacking from a data-centric perspective, neglecting crucial aspects of the models. To address this issue, we propose a novel approach in this paper, coined Decision Boundary Adaptation (DBA). Our approach innovatively adopts a model-centric viewpoint, leveraging operations on the model to attain properties that enhance transferability. We observe that a flatter curvature of the statistical manifold, influenced by both samples and model parameters, leads to stronger transferability of the adversarial attacks. To leverage this, we introduce the concept of local flatness, providing an evaluation method for local flatness property along with a detailed mathematical proof. Additionally, we demonstrate a consistent relationship between local flatness, the model’s decision boundary, and the gradient descent process, showing how flatness can be achieved through gradient descent at the model parameter level. Through extensive evaluation using state-of-the-art adversarial attack techniques, our DBA approach significantly enhances the black-box attack capabilities of all the tested adversarial attack methods. The implementation of our method is available at https: //github. com/LMBTough/DBA.

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ICML Conference 2025 Conference Paper

Splitting & Integrating: Out-of-Distribution Detection via Adversarial Gradient Attribution

Jiayu Zhang 0001
Xinyi Wang 0005
Zhibo Jin
Zhiyu Zhu
Jianlong Zhou
Fang Chen 0001
Huaming Chen

Out-of-distribution (OOD) detection is essential for enhancing the robustness and security of deep learning models in unknown and dynamic data environments. Gradient-based OOD detection methods, such as GAIA, analyse the explanation pattern representations of in-distribution (ID) and OOD samples by examining the sensitivity of model outputs w. r. t. model inputs, resulting in superior performance compared to traditional OOD detection methods. However, we argue that the non-zero gradient behaviors of OOD samples do not exhibit significant distinguishability, especially when ID samples are perturbed by random perturbations in high-dimensional spaces, which negatively impacts the accuracy of OOD detection. In this paper, we propose a novel OOD detection method called S & I based on layer S plitting and gradient I ntegration via Adversarial Gradient Attribution. Specifically, our approach involves splitting the model’s intermediate layers and iteratively updating adversarial examples layer-by-layer. We then integrate the attribution gradients from each intermediate layer along the attribution path from adversarial examples to the actual input, yielding true explanation pattern representations for both ID and OOD samples. Experiments demonstrate that our S & I algorithm achieves state-of-the-art results, with the average FPR95 of 29. 05% (ResNet34)/38. 61% (WRN40) and 37. 31% (BiT-S) on the CIFAR100 and ImageNet benchmarks, respectively. Our code is available at: https: //github. com/LMBTough/S-Ihttps: //github. com/LMBTough/S-I

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AAAI Conference 2025 Conference Paper

Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling

Haoran Li
Xingjian Li
Jiahua Shi
Huaming Chen
Bo Du
Daisuke Kihara
Johan Barthelemy
Jun Shen

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET image segmentation tasks remains challenging due to two main issues: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, directly collected from raw-data from the real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars. In contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this work, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on the improved Bilateral Filter to alleviate the domain shift problem. More importantly, we construct the first UDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods.

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ICLR Conference 2024 Conference Paper

AttEXplore: Attribution for Explanation with model parameters eXploration

Zhiyu Zhu
Huaming Chen
Jiayu Zhang 0001
Xinyi Wang 0005
Zhibo Jin
Jason Xue 0002
Flora D. Salim

Due to the real-world noise and human-added perturbations, attaining the trustworthiness of deep neural networks (DNNs) is a challenging task. Therefore, it becomes essential to offer explanations for the decisions made by these non-linear and complex parameterized models. Attribution methods are promising for this goal, yet its performance can be further improved. In this paper, for the first time, we present that the decision boundary exploration approaches of attribution are consistent with the process for transferable adversarial attacks. Specifically, the transferable adversarial attacks craft general adversarial samples from the source model, which is consistent with the generation of adversarial samples that can cross multiple decision boundaries in attribution. Utilizing this consistency, we introduce a novel attribution method via model parameter exploration. Furthermore, inspired by the capability of frequency exploration to investigate the model parameters, we provide enhanced explainability for DNNs by manipulating the input features based on frequency information to explore the decision boundaries of different models. Large-scale experiments demonstrate that our \textbf{A}ttribution method for \textbf{E}xplanation with model parameter e\textbf{X}ploration (AttEXplore) outperforms other state-of-the-art interpretability methods. Moreover, by employing other transferable attack techniques, AttEXplore can explore potential variations in attribution outcomes. Our code is available at: https://github.com/LMBTough/ATTEXPLORE.

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ICLR Conference 2024 Conference Paper

Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation

Zhiyu Zhu
Xinyi Wang 0005
Zhibo Jin
Jiayu Zhang 0001
Huaming Chen

Vision Transformers (ViTs) have been widely used in various domains. Similar to Convolutional Neural Networks (CNNs), ViTs are prone to the impacts of adversarial samples, raising security concerns in real-world applications. As one of the most effective black-box attack methods, transferable attacks can generate adversarial samples on surrogate models to directly attack the target model without accessing the parameters. However, due to the distinct internal structures of ViTs and CNNs, adversarial samples constructed by traditional transferable attack methods may not be applicable to ViTs. Therefore, it is imperative to propose more effective transferability attack methods to unveil latent vulnerabilities in ViTs. Existing methods have found that applying gradient regularization to extreme gradients across different functional regions in the transformer structure can enhance sample transferability. However, in practice, substantial gradient disparities exist even within the same functional region across different layers. Furthermore, we find that mild gradients therein are the main culprits behind reduced transferability. In this paper, we introduce a novel Gradient Normalization Scaling method for fine-grained gradient editing to enhance the transferability of adversarial attacks on ViTs. More importantly, we highlight that ViTs, unlike traditional CNNs, exhibit distinct attention regions in the frequency domain. Leveraging this insight, we delve into exploring the frequency domain to further enhance the algorithm's transferability. Through extensive experimentation on various ViT variants and traditional CNN models, we substantiate that the new approach achieves state-of-the-art performance, with an average performance improvement of 33.54\% and 42.05\% on ViT and CNN models, respectively. Our code is available at: https://github.com/LMBTough/GNS-HFA.

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ICML Conference 2024 Conference Paper

Iterative Search Attribution for Deep Neural Networks

Zhiyu Zhu
Huaming Chen
Xinyi Wang 0005
Jiayu Zhang 0001
Zhibo Jin
Jason Xue 0002
Jun Shen 0001

Deep neural networks (DNNs) have achieved state-of-the-art performance across various applications. However, ensuring the reliability and trustworthiness of DNNs requires enhanced interpretability of model inputs and outputs. As an effective means of Explainable Artificial Intelligence (XAI) research, the interpretability of existing attribution algorithms varies depending on the choice of reference point, the quality of adversarial samples, or the applicability of gradient constraints in specific tasks. To thoroughly explore the attribution integration paths, in this paper, inspired by the iterative generation of high-quality samples in the diffusion model, we propose an Iterative Search Attribution (ISA) method. To enhance attribution accuracy, ISA distinguishes the importance of samples during gradient ascent and descent, while clipping the relatively unimportant features in the model. Specifically, we introduce a scale parameter during the iterative process to ensure the features in next iteration are always more significant than those in current iteration. Comprehensive experimental results show that our method has superior interpretability in image recognition tasks compared with state-of-the-art baselines. Our code is available at: https: //github. com/LMBTough/ISA

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AAAI Conference 2024 Conference Paper

MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks

Zhiyu Zhu
Huaming Chen
Jiayu Zhang
Xinyi Wang
Zhibo Jin
Minhui Xue
Dongxiao Zhu
Kim-Kwang Raymond Choo

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.

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