Author name cluster

Chun Yang

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Dual-Geometry Graph Network: Unifying Local and Global Priors for Few-Shot Learning

Zheng Han
Xiaobin Zhu
Chun Yang
Jingyan Qin
Xu-Cheng Yin

In few-shot learning, utilizing local and global geometric priors to capture both subtle local class metrics and coarse global structures within the meta-task are important to obtain discriminative embeddings. However, existing graph-based and curvature-based few-shot approaches only focus on either one kind of geometric prior but neglect the other. To effectively utilize the pros of these two paradigms, we propose a novel Dual-Geometry Graph Network (DGGN) to adaptively integrate the local and global geometric priors via two key pathways. Specifically, the local-wise metric modeling pathway utilizes Ollivier-Ricci curvature to capture task-specific local class metrics among the instances, and the global-wise connectivity modeling pathway utilizes resistive embedding to capture global instance distributions and connectivity patterns of the entire meta-task. In addition, we introduce two new regularization loss functions to explicitly enhance the geometric representation ability of the local and global pathways respectively. We validate that DGGN's superior performance stems from its adaptively topological refinements by measuring the graph edit distance, demonstrating its ability to match the underlying data distribution. Extensive experiments show that DGGN sets a new state-of-the-art on standard, cross-domain, and semi-supervised few-shot benchmarks.

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JBHI Journal 2025 Journal Article

A Drug-Drug Interaction Prediction Method Based on Atomic 3D Position Encoding and Elastic Message Passing Graph Neural Network

Tao Luo
Tao Lin
Chun Yang
Lingjie Fan
Wei Wang

Drug-drug interaction (DDI) refers to the inhibitory or enhancing effects between different drugs. Existing DDI prediction methods primarily use graph neural networks (GNNs) to directly represent drug molecular features. However, they often ignore the 3D structures of different atoms within drug molecules and the impact of noise in GNNs on DDI prediction. Consequently, the accuracy of GNN-based DDI prediction remains unsatisfactory. To address these limitations, this study proposes a DDI prediction method based on atomic 3D position encoding and an elastic message passing graph neural network (A3DPE-EMPGNN). Firstly, we construct an atomic feature network based on an attention mechanism and a message passing neural network. This network leverages 3D position encoding based on the molecular centroid to learn the features of different atoms and their associated chemical bonds, thereby constructing a graph-based molecular representation. Secondly, we design a molecular feature network that incorporates an attention mechanism, utilizing multi-head attention to capture interaction information between different drug molecules. Thirdly, we employ an adversarial attack detection and defense strategy, integrating supervised and contrastive loss learning to optimize the model and enhance its robustness while performing DDI prediction. Lastly, we evaluate the effectiveness of A3DPE-EMPGNN on two real-world datasets. Experimental results clearly demonstrate that our method achieves over 98% accuracy across ACC, AUC, AP, and F1-score, outperforming state-of-the-art GNN-based models.

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AAAI Conference 2022 Conference Paper

Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification

Zhiyu Fang
Xiaobin Zhu
Chun Yang
Zheng Han
Jingyan Qin
Xu-Cheng Yin

Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due to the lack of fine-grained instance-wise annotations, it still easily suffer from the domain shift problem for the discrepancy between the visual representation of diversified images and the semantic representation of fixed attributes. In this paper, we propose an innovative autoencoder network by learning Aligned Cross-Modal Representations (dubbed ACMR) for GZSC. Specifically, we propose a novel Vision- Semantic Alignment (VSA) method to strengthen the alignment of cross-modal latent features on the latent subspaces guided by a learned classifier. In addition, we propose a novel Information Enhancement Module (IEM) to reduce the possibility of latent variables collapse meanwhile encouraging the discriminative ability of latent variables. Extensive experiments on publicly available datasets demonstrate the stateof-the-art performance of our method.

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ICRA Conference 2020 Conference Paper

IF-Net: An Illumination-invariant Feature Network

Po-Heng Chen
Zhao-Xu Luo
Zu-Kuan Huang
Chun Yang
Kuan-Wen Chen

Feature descriptor matching is a critical step is many computer vision applications such as image stitching, image retrieval and visual localization. However, it is often affected by many practical factors which will degrade its performance. Among these factors, illumination variations are the most influential one, and especially no previous descriptor learning works focus on dealing with this problem. In this paper, we propose IF-Net, aimed to generate a robust and generic descriptor under crucial illumination changes conditions. We find out not only the kind of training data important but also the order it is presented. To this end, we investigate several dataset scheduling methods and propose a separation training scheme to improve the matching accuracy. Further, we propose a ROI loss and hard-positive mining strategy along with the training scheme, which can strengthen the ability of generated descriptor dealing with large illumination change conditions. We evaluate our approach on public patch matching benchmark and achieve the best results compared with several state-of-the-arts methods. To show the practicality, we further evaluate IF-Net on the task of visual localization under large illumination changes scenes, and achieves the best localization accuracy.

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