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Hongyang Chen

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15 papers

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AAAI Conference 2026 Conference Paper

Unsupervised Contrastive Learning for Efficient and Robust Spectral Shape Matching

Feifan Luo
Hongyang Chen

Estimating correspondences between pairs of non-rigid deformable 3D shapes remains a significant challenge in computer vision and graphics. While deep functional map methods have become the go-to solution for addressing this problem, they primarily focus on optimizing pointwise and functional maps either individually or jointly, rather than directly enhancing feature representations in the embedding space, which often results in inadequate feature quality and suboptimal matching performance. Furthermore, these approaches heavily rely on traditional functional map techniques, such as time-consuming functional map solvers, which incur substantial computational costs. In this work, we introduce, for the first time, a novel unsupervised contrastive learning-based approach for efficient and robust 3D shape matching. We begin by presenting an unsupervised contrastive learning framework that promotes feature learning by maximizing consistency within positive similarity pairs and minimizing it within negative similarity pairs, thereby improving both the consistency and discriminability of the learned features. We then design a significantly simplified functional map learning architecture that eliminates the need for computationally expensive functional map solvers and multiple auxiliary functional map losses, greatly enhancing computational efficiency. By integrating these two components into a unified two-branch pipeline, our method achieves state-of-the-art performance in both accuracy and efficiency. Extensive experiments demonstrate that our approach is not only computationally efficient but also outperforms current state-of-the-art methods across various challenging benchmarks, including near-isometric, non-isometric, and topologically inconsistent scenarios—even surpassing supervised techniques.

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AAAI Conference 2025 Conference Paper

CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation

Yuxuan Wang
Yijun Liu
Fei Yu
Chen Huang
Kexin Li
Zhiguo Wan
Wanxiang Che
Hongyang Chen

Despite the rapid development of Chinese vision-language models (VLMs), most existing Chinese vision-language (VL) datasets are constructed on Western-centric images from existing English VL datasets. The cultural bias in the images makes these datasets unsuitable for evaluating VLMs in Chinese culture. To remedy this issue, we present a new Chinese Vision-Language Understanding Evaluation (CVLUE) benchmark dataset, where the selection of object categories and images is entirely driven by Chinese native speakers, ensuring that the source images are representative of Chinese culture. The benchmark contains four distinct VL tasks ranging from image-text retrieval to visual question answering, visual grounding and visual dialogue. We present a detailed statistical analysis of CVLUE and provide a baseline performance analysis with several open-source multilingual VLMs on CVLUE and its English counterparts to reveal their performance gap between English and Chinese. Our in-depth category-level analysis reveals a lack of Chinese cultural knowledge in existing VLMs. We also find that fine-tuning on Chinese culture-related VL datasets effectively enhances VLMs' understanding of Chinese culture.

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AAAI Conference 2025 Conference Paper

Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning

Hui Fang
Yang Gao
Peng Zhang
Jiangchao Yao
Hongyang Chen
Haishuai Wang

Personalized federated learning (PFL) on graphs is an emerging field focusing on the collaborative development of architectures across multiple clients, each with distinct graph data distributions while adhering to strict privacy standards. This area often requires extensive expert intervention in model design, which is a significant limitation. Recent advancements have aimed to automate the search for graph neural network architectures, incorporating large language models (LLMs) for their advanced reasoning and self-reflection capabilities. However, two technical challenges persist. First, although LLMs are effective in natural language processing, their ability to meet the complex demands of graph neural architecture search (GNAS) is still being explored. Second, while LLMs can guide the architecture search process, they do not directly solve the issue of client drift due to heterogeneous data distributions. To address these challenges, we introduce a novel method, Personalized Federated Graph Neural Architecture Search (PFGNAS). This approach employs a task-specific prompt to identify and integrate optimal GNN architectures continuously. To counteract client drift, PFGNAS utilizes a weight-sharing strategy of supernet, which optimizes the local architectures while ensuring client-specific personalization. Extensive evaluations show that PFGNAS significantly outperforms traditional PFL methods, highlighting the advantages of integrating LLMs into personalized federated learning environments.

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JBHI Journal 2025 Journal Article

Multi-View Fused Nonnegative Matrix Completion Methods for Drug-Target Interaction Prediction

Ting Li
Chuanqi Lao
Zhao Li
Hongyang Chen

Accurate prediction of drug-target interactions (DTIs) is crucial for accelerating drug discovery and reducing experimental costs. However, challenges such as sparse interactions and heterogeneous datasets complicate this prediction. In this study, we hypothesize that leveraging nonnegative matrix completion and integrating heterogeneous similarity information from multiple biological views can improve the accuracy, interpretability, and scalability of DTI prediction. To validate this, we propose two multi-view fused nonnegative matrix completion methods that combine three key components: (1) a nonnegative matrix completion framework that avoids heuristic rank selection and ensures biologically interpretable predictions; (2) a linear multi-view fusion mechanism, where weights over multiple drug and target similarity matrices are jointly learned through linearly constrained quadratic programming; and (3) multi-graph Laplacian regularization to preserve structural properties within each view. The optimization is performed using two efficient proximal linearization-incorporated block coordinate descent algorithms. Extensive experiments on four gold-standard datasets and a larger real-world dataset demonstrate that our models consistently outperform state-of-the-art single-view, multi-view and deep learning-based DTI prediction methods. Furthermore, ablation studies confirm the contribution of each model component, and scalability analysis highlights the computational efficiency of our approach.

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AAAI Conference 2024 Conference Paper

CutFreq: Cut-and-Swap Frequency Components for Low-Level Vision Augmentation

Hongyang Chen
Kaisheng Ma

Low-level vision plays a crucial role in a wide range of imaging quality and image recognition applications. However, the limited size, quality, and diversity of datasets often pose significant challenges for low-level tasks. Data augmentation is the most effective and practical way of sample expansion, but the commonly used augmentation methods in high-level tasks have limited improvement in the low-level due to the boundary effects or the non-realistic context information. In this paper, we propose the Cut-and-Swap Frequency Components (CutFreq) method for low-level vision, which aims to preserve high-level representations with directionality and improve image synthesis quality. Observing the significant frequency domain differences between reconstructed images and real ones, in CutFreq, we propose to transform the input and real images separately in the frequency domain, then define two stages for the model training process, and finally swap the specified frequency bands respectively and inversely transform to generate augmented samples. The experimental results show the superior performance of CutFreq on five low-level vision tasks. Moreover, we demonstrate the effectiveness of CutFreq in the low-data regime. Code is available at https://github.com/DreamerCCC/CutFreq.

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AAAI Conference 2024 Conference Paper

Depression Detection via Capsule Networks with Contrastive Learning

Han Liu
Changya Li
Xiaotong Zhang
Feng Zhang
Wei Wang
Fenglong Ma
Hongyang Chen
Hong Yu

Depression detection is a challenging and crucial task in psychological illness diagnosis. Utilizing online user posts to predict whether a user suffers from depression seems an effective and promising direction. However, existing methods suffer from either poor interpretability brought by the black-box models or underwhelming performance caused by the completely separate two-stage model structure. To alleviate these limitations, we propose a novel capsule network integrated with contrastive learning for depression detection (DeCapsNet). The highlights of DeCapsNet can be summarized as follows. First, it extracts symptom capsules from user posts by leveraging meticulously designed symptom descriptions, and then distills them into class-indicative depression capsules. The overall workflow is in an explicit hierarchical reasoning manner and can be well interpreted by the Patient Health Questionnaire-9 (PHQ9), which is one of the most widely adopted questionnaires for depression diagnosis. Second, it integrates with contrastive learning, which can facilitate the embeddings from the same class to be pulled closer, while simultaneously pushing the embeddings from different classes apart. In addition, by adopting the end-to-end training strategy, it does not necessitate additional data annotation, and mitigates the potential adverse effects from the upstream task to the downstream task. Extensive experiments on three widely-used datasets show that in both within-dataset and cross-dataset scenarios our proposed method outperforms other strong baselines significantly.

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AAAI Conference 2024 Conference Paper

Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation

Can Xu
Haosen Wang
Weigang Wang
Pengfei Zheng
Hongyang Chen

Denoising diffusion models have shown great potential in multiple research areas. Existing diffusion-based generative methods on de novo 3D molecule generation face two major challenges. Since majority heavy atoms in molecules allow connections to multiple atoms through single bonds, solely using pair-wise distance to model molecule geometries is insufficient. Therefore, the first one involves proposing an effective neural network as the denoising kernel that is capable to capture complex multi-body interatomic relationships and learn high-quality features. Due to the discrete nature of graphs, mainstream diffusion-based methods for molecules heavily rely on predefined rules and generate edges in an indirect manner. The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds. In our research, we view the iterative way of updating molecule conformations in diffusion process is consistent with molecular dynamics and introduce a novel molecule generation method named Geometric-Facilitated Molecular Diffusion (GFMDiff). For the first challenge, we introduce a Dual-track Transformer Network (DTN) to fully excevate global spatial relationships and learn high quality representations which contribute to accurate predictions of features and geometries. As for the second challenge, we design Geometric-facilitated Loss (GFLoss) which intervenes the formation of bonds during the training period, instead of directly embedding edges into the latent space. Comprehensive experiments on current benchmarks demonstrate the superiority of GFMDiff.

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AAAI Conference 2024 Conference Paper

Guiding a Harsh-Environments Robust Detector via RAW Data Characteristic Mining

Hongyang Chen
Hung-Shuo Tai
Kaisheng Ma

Consumer-grade cameras capture the RAW physical description of a scene and then process the image signals to obtain high-quality RGB images that are faithful to human visual perception. Conventionally, dense prediction scenes require high-precision recognition of objects in RGB images. However, predicting RGB data to exhibit the expected adaptability and robustness in harsh environments can be challenging. By capitalizing on the broader color gamut and higher bit depth offered by RAW data, in this paper, we demonstrate that RAW data can significantly improve the accuracy and robustness of object detectors in harsh environments. Firstly, we propose a general Pipeline for RAW Detection (PRD), along with a preprocessing strategy tailored to RAW data. Secondly, we design the RAW Corruption Benchmark (RCB) to address the dearth of benchmarks that reflect realistic scenarios in harsh environments. Thirdly, we demonstrate the significant improvement of RAW images in object detection for low-light and corrupt scenes. Specifically, our experiments indicate that PRD (using FCOS) outperforms RGB detection by 13.9mAP on LOD-Snow without generating restored images. Finally, we introduce a new nonlinear method called Functional Regularization (FR), which can effectively mine the unique characteristics of RAW data. The code is available at https://github.com/DreamerCCC/RawMining.

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AAAI Conference 2024 Conference Paper

Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing

Han Liu
Siyang Zhao
Xiaotong Zhang
Feng Zhang
Wei Wang
Fenglong Ma
Hongyang Chen
Hong Yu

Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all. While prevailing methods have shown promising performance via transferring knowledge from seen classes to unseen classes, they are still limited by (1) Inherent dissimilarities among classes make the transformation of features learned from seen classes to unseen classes both difficult and inefficient. (2) Rare labeled novel samples usually cannot provide enough supervision signals to enable the model to adjust from the source distribution to the target distribution, especially for complicated scenarios. To alleviate the above issues, we propose a simple and effective strategy for few-shot and zero-shot text classification. We aim to liberate the model from the confines of seen classes, thereby enabling it to predict unseen categories without the necessity of training on seen classes. Specifically, for mining more related unseen category knowledge, we utilize a large pre-trained language model to generate pseudo novel samples, and select the most representative ones as category anchors. After that, we convert the multi-class classification task into a binary classification task and use the similarities of query-anchor pairs for prediction to fully leverage the limited supervision signals. Extensive experiments on six widely used public datasets show that our proposed method can outperform other strong baselines significantly in few-shot and zero-shot tasks, even without using any seen class samples.

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IJCAI Conference 2024 Conference Paper

MPGraf: a Modular and Pre-trained Graphformer for Learning to Rank at Web-scale (Extended Abstract)

Yuchen Li
Haoyi Xiong
Linghe Kong
Zeyi Sun
Hongyang Chen
Shuaiqiang Wang
Dawei Yin

Both Transformer and Graph Neural Networks (GNNs) have been used in learning to rank (LTR), however, they adhere to two distinct yet complementary problem formulations, i. e. , ranking score regression based on query-webpage pairs and link prediction within query-webpage bipartite graphs, respectively. Though it is possible to pre-train GNNs or Transformers on source datasets and fine-tune them subject to sparsely annotated LTR datasets separately, the source-target distribution shifts across the pairs and bipartite graphs domains make it extremely difficult to integrate these diverse models into a single LTR framework at a web-scale. We introduce the novel MPGraf model, which utilizes a modular and capsule-based pre-training approach, aiming to incorporate regression capacities from Transformers and link prediction capabilities of GNNs cohesively. We conduct extensive experiments to evaluate the performance of MPGraf using real-world datasets collected from large-scale search engines. The results show that MPGraf can outperform baseline algorithms on several major metrics. Further, we deploy and evaluate MPGraf atop a large-scale search engine with realistic web traffic via A/B tests, where we can still observe significant improvement. MPGraf performs consistently in both offline and online evaluations.

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IJCAI Conference 2023 Conference Paper

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

Han Liu
Xingshuo Huang
Xiaotong Zhang
Qimai Li
Fenglong Ma
Wei Wang
Hongyang Chen
Hong Yu

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors to facilitate score-based methods to obtain better results. However, these gradient priors still suffer from the edge gradient discrepancy issue and the successive iteration gradient direction issue, thus are difficult to simply extend to decision-based methods. In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. First, by leveraging the joint bilateral filter to deal with each random perturbation, DBA-GP can guarantee that the generated perturbations in edge locations are hardly smoothed, i. e. , alleviating the edge gradient discrepancy, thus remaining the characteristics of the original image as much as possible. Second, by utilizing a new gradient updating strategy to automatically adjust the successive iteration gradient direction, DBA-GP can accelerate the convergence speed, thus improving the query efficiency. Extensive experiments have demonstrated that the proposed method outperforms other strong baselines significantly.

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AAAI Conference 2023 Conference Paper

Boosting Few-Shot Text Classification via Distribution Estimation

Han Liu
Feng Zhang
Xiaotong Zhang
Siyang Zhao
Fenglong Ma
Xiao-ming Wu
Hongyang Chen
Hong Yu

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.

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NeurIPS Conference 2023 Conference Paper

HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

Han Liu
Zhi Xu
Xiaotong Zhang
Feng Zhang
Fenglong Ma
Hongyang Chen
Hong Yu
Xianchao Zhang

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible. Research on this problem is still in the embryonic stage and only a few methods are available. Nevertheless, existing methods rely on the complex heuristic algorithm or unreliable gradient estimation strategy, which probably fall into the local optimum and inevitably consume numerous queries, thus are difficult to craft satisfactory adversarial examples with high semantic similarity and low perturbation rate in a limited query budget. To alleviate above issues, we propose a simple yet effective framework to generate high quality textual adversarial examples under the black-box hard-label attack scenarios, named HQA-Attack. Specifically, after initializing an adversarial example randomly, HQA-attack first constantly substitutes original words back as many as possible, thus shrinking the perturbation rate. Then it leverages the synonym set of the remaining changed words to further optimize the adversarial example with the direction which can improve the semantic similarity and satisfy the adversarial condition simultaneously. In addition, during the optimizing procedure, it searches a transition synonym word for each changed word, thus avoiding traversing the whole synonym set and reducing the query number to some extent. Extensive experimental results on five text classification datasets, three natural language inference datasets and two real-world APIs have shown that the proposed HQA-Attack method outperforms other strong baselines significantly.

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NeurIPS Conference 2023 Conference Paper

Learning Invariant Molecular Representation in Latent Discrete Space

Xiang Zhuang
Qiang Zhang
Keyan Ding
Yatao Bian
Xiao Wang
Jingsong Lv
Hongyang Chen
Huajun Chen

Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments. To address this issue, we propose a new framework for learning molecular representations that exhibit invariance and robustness against distribution shifts. Specifically, we propose a strategy called ``first-encoding-then-separation'' to identify invariant molecule features in the latent space, which deviates from conventional practices. Prior to the separation step, we introduce a residual vector quantization module that mitigates the over-fitting to training data distributions while preserving the expressivity of encoders. Furthermore, we design a task-agnostic self-supervised learning objective to encourage precise invariance identification, which enables our method widely applicable to a variety of tasks, such as regression and multi-label classification. Extensive experiments on 18 real-world molecular datasets demonstrate that our model achieves stronger generalization against state-of-the-art baselines in the presence of various distribution shifts. Our code is available at https: //github. com/HICAI-ZJU/iMoLD.

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AAAI Conference 2023 Conference Paper

SSPAttack: A Simple and Sweet Paradigm for Black-Box Hard-Label Textual Adversarial Attack

Han Liu
Zhi Xu
Xiaotong Zhang
Xiaoming Xu
Feng Zhang
Fenglong Ma
Hongyang Chen
Hong Yu

Hard-label textual adversarial attack is a challenging task, as only the predicted label information is available, and the text space is discrete and non-differentiable. Relevant research work is still in fancy and just a handful of methods are proposed. However, existing methods suffer from either the high complexity of genetic algorithms or inaccurate gradient estimation, thus are arduous to obtain adversarial examples with high semantic similarity and low perturbation rate under the tight-budget scenario. In this paper, we propose a simple and sweet paradigm for hard-label textual adversarial attack, named SSPAttack. Specifically, SSPAttack first utilizes initialization to generate an adversarial example, and removes unnecessary replacement words to reduce the number of changed words. Then it determines the replacement order and searches for an anchor synonym, thus avoiding going through all the synonyms. Finally, it pushes substitution words towards original words until an appropriate adversarial example is obtained. The core idea of SSPAttack is just swapping words whose mechanism is simple. Experimental results on eight benchmark datasets and two real-world APIs have shown that the performance of SSPAttack is sweet in terms of similarity, perturbation rate and query efficiency.

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