Hongyuan Zhang (张鸿远)

Hongyuan Zhang (张鸿远)

PostDoc at HKU

Publications

The full list of my papers can be found on my Google Scholar. Here are some selected papers:

(*: Corresponding authors; +: Equal contribution)

  • Hongyuan Zhang, Yanchen Xu, Sida Huang, and Xuelong Li, “Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise,” in International Conference on Machine Learning (ICML), 2026.
  • Ruishu Zhu, Zhihao Huang, Jiacheng Sun, Ping Luo, Hongyuan Zhang*, and Xuelong Li*, “ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Diffusion Models,” in International Conference on Machine Learning (ICML), 2026.
  • Qing Zhou, Hongyuan Zhang, Tao Yang, Junyu Gao, and Qi Wang, “Statistically Optimal Scaling for Token Merging in Transformers,” in International Conference on Machine Learning (ICML), 2026.
  • Da Zhang, Bingyu Li, Zhiyuan Zhao, Hongyuan Zhang, Junyu Gao, and Xuelong Li, “MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification,” in International Conference on Machine Learning (ICML), 2026.
  • Yuheng Lei, Sitong Mao, Shunbo Zhou, Hongyuan Zhang, Xuelong Li, and Ping Luo, “Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning,” Transactions on Machine Learning Research (TMLR), 2026.
  • Xukun Zhou , Fengxin Li , Ming Chen, Yan Zhou, Pengfei Wan, Yeying Jin, Hongyuan Zhang, Hongyan Liu, Zhaoxin Fan, Jun He, and Xuelong Li, “ExGes: Expressive Human Motion Retrieval and Modulation for Audio-Driven Gesture Synthesis,” IEEE Transactions on Visualization and Computer Graphics (TVCG), 2026.
  • Xudong Cai, Yongcai Wang, Zhaoxin Fan, Haoran Deng, Shuo Wang, Wanting Li, Deying Li, Lun Luo, Minhang Wang, Hongyuan Zhang, and Xuelong Li, “Dust to Tower: Coarse-to-Fine Photo-Realistic Scene Reconstruction from Sparse Uncalibrated Images,” IEEE Transactions on Visualization and Computer Graphics (TVCG), 2026.
  • Yanan Zhu, Ziwei Xiang, Jinyang Guo, Jiamin Wu, Chunfeng Song, Hongyuan Zhang, Hongjian Fang, Qihao Zheng, Yufei Guo, and Xianglong Liu, “Region-Aware Hierarchical Sub-Feature Alignment for Robust EEG-Based Visual Decoding,” in Findings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR Findings), 2026.
  • Qing Zhou, Bingxuan Zhao, Tao Yang, Hongyuan Zhang, Junyu Gao, and Qi Wang, “Batch Loss Score for Dynamic Data Pruning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
  • Zhiqian Lan, Yuxuan Jiang, Ruiqi Wang, Xuanbing Xie, Rongkui Zhang, Yicheng Zhu, Peihang Li, Tianshuo Yang, Tianxing Chen, Haoyu Gao, Xiaokang Yang, Xuelong Li, Hongyuan Zhang, Yao Mu, and Ping Luo, “AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory,” in The Fourteenth International Conference on Learning Representations (ICLR), 2026.
  • Qing Zhou, Tao Yang, Bingxuan Zhao, Hongyuan Zhang, Junyu Gao, and Qi Wang, “Inconsistency Biases in Dynamic Data Pruning,” in The Fourteenth International Conference on Learning Representations (ICLR), 2026.
  • Zhenyu Gu, Yanchen Xu, Sida Huang, Yubin Guo, and Hongyuan Zhang*, “Rectified Noise: A Generative Model Using Positive-incentive Noise,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), oral, 2026.
  • Siqi Huang, Sida Huang, and Hongyuan Zhang*, “CoLM: Collaborative Large Models via A Client-Server Paradigm,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), oral, 2026.
  • Sida Huang, Siqi Huang, Ping Luo, and Hongyuan Zhang*, “Laytrol: Preserving Pretrained Knowledge in Layout Control for Multimodal Diffusion Transformers,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026.
  • Ruishu Zhu, Sida Huang, Ziheng Jiao, and Hongyuan Zhang*, “Explore How To Inject Beneficial Noise in MLLMs,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026.
  • Kai Jiang, Zhengyan Shi, Dell Zhang, Hongyuan Zhang*, and Xuelong Li*, “MiN: Mixture of Noise for Pre-Trained Model-Based Class-Incremental Learning,” in The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
  • Zhihao Huang, Xi Qiu, Yukuo Ma, Yifu Zhou, Hongyuan Zhang*, Chi Zhang*, and Xuelong Li*, “NFIG: Multi-Scale Autoregressive Image Generation via Frequency Ordering,” in The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
  • Jiquan Shan, Junxiao Wang, Lifeng Zhao, Liang Cai, Hongyuan Zhang, and Ioannis Liritzis, “AnchorFormer: Differentiable Anchor Attention for Efficient Vision Transformer,” Pattern Recognition Letters, 2025.
  • Chuang Yang, Xu Han, Tao Han, Yuejiao Su, Junyu Gao, Hongyuan Zhang, Yi Wang, and Lap-Pui Chau, “SignEye: Traffic Sign Interpretation from Vehicle First-Person View,” IEEE Transactions on Intelligent Transportation Systems(T-ITS), 2025.
  • Ziheng Jiao, Hongyuan Zhang*, and Xuelong Li*, “CNN2GNN: How to Bridge CNN with GNN,” IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), 2025.
  • Hongyuan Zhang, Sida Huang, Yubin Guo, and Xuelong Li, “Variational Positive-incentive Noise: How Noise Benefits Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), 2025.
  • Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Qiwei Liang, Yao Mu, Hongyuan Zhang, Wenqi Shao, and Ping Luo “Text2World: Benchmarking Large Language Models for Symbolic World Model Generation,” ACL Findings, 2025.
  • Siqi Huang, Yanchen Xu, Hongyuan Zhang*, and Xuelong Li*, “Learn Positive-incentive Noise As Graph Augmentation,” in International Conference on Machine Learning (ICML), 2025.
  • Junying Wang, Hongyuan Zhang, and Yuan Yuan, “Adv-PCG: Facial Adversarial Attacks for Customized Portrait Generation”, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), oral , 2025. (~3.3% of the accepted papers)
  • Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen, Shijia Peng, Qiangyu Chen, Mingkun Xu, Ruizhen Hu, Hongyuan Zhang, Xuelong Li, and Ping Luo, “G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation”, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  • Ziheng Jiao, Hongyuan Zhang*, and Xuelong Li*, “Deep Graph Multi-View Representation Learning with Self-Augmented Weight Fusion”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 36, no. 8, pp. 14119–14130, 2025.
  • Sida Huang, Hongyuan Zhang*, and Xuelong Li*, “Enhance Vision-Language Alignment with Noise”, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2025.
  • Yanchen Xu, Siqi Huang, Hongyuan Zhang*, and Xuelong Li*, “Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?”, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2025.
  • Yujun Li, Hongyuan Zhang, and Yuan Yuan, “Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model”, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2025.
  • Junying Wang, Hongyuan Zhang, Hongwei Wang, and Yuan Yuan, “Graph Convolutional Network with Self-Augmented Weights for Semi-Supervised Multi-View Learning”, IEEE Transactions on Neural Network and Learning Systems (T-NNLS), , vol. 36, no. 7, pp. 12257–12270, 2025.
  • Hongyuan Zhang, Yanan Zhu, and Xuelong Li, “Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), vol. 46, no. 11, pp. 7451–7462, 2024.
  • Ziheng Jiao, Hongyuan Zhang*, and Xuelong Li*, “CNN to GNN: Unsupervised Multi-level Knowledge Learning”, in Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM), 2024.
  • Hongyuan Zhang, Ziheng Jiao, and Xuelong Li, “Orthogonal Subspace Exploration for Matrix Completion”, Pattern Recognition, vol. 153, pp. 110456, 2024.
  • Ziheng Jiao, Hongyuan Zhang, Rui Zhang, and Xuelong Li, “Graph Manifold Learning with Non-Gradient Decision Layer”, Neurocomputing, 2024.
  • Hongyuan Zhang, Jiankun Shi, Rui Zhang, and Xuelong Li, “Non-Graph Data Clustering via O(n) Bipartite Graph Convolution”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), vol.45, no. 7, pp. 8729–8742, 2023.
  • Xuelong Li, Hongyuan Zhang, and Rui Zhang, “Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), vol. 45, no. 2, pp. 1981–1991, 2023.
  • Hongyuan Zhang, Yanan Zhu, and Xuelong Li, “Towards Projected Clustering with Aggregated Mapping”, IEEE Transactions on Image Processing (T-IP), vol. 32, pp. 4103–4113, 2023.
  • Hongyuan Zhang, Pei Li, Rui Zhang, and Xuelong Li, “Embedding Graph Auto-Encoder for Graph Clustering”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 34, no. 11, pp. 9352–9362, 2023.
  • Hongyuan Zhang and Xuelong Li, “Discretize Relaxed Solution of Spectral Clustering via A Non-Heuristic Algorithm”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 35, no. 12, pp. 17965–17972, 2024.
  • Ziheng Jiao +, Hongyuan Zhang +, and Xuelong Li, “Learn Topological Representation with Flexible Manifold Layer”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
  • Rui Zhang, Ziheng Jiao, Hongyuan Zhang, and Xuelong Li, “Non-Gradient Manifold Neural Network”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), vol. 45, no. 3, pp.3986–3993, 2023.
  • Xuelong Li, Pei Li, Hongyuan Zhang, Kangjia Zhu, and Rui Zhang, “Pivotal-Aware Principal Component Analysis”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 35, no. 9, pp. 12201–12210, 2023.
  • Rui Zhang, Hongyuan Zhang, and Xuelong Li, “Maximum Joint Probability With Multiple Representations for Clustering”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 33, no. 9, pp. 4300–4310, 2022.
  • Xuelong Li, Hongyuan Zhang, and Rui Zhang, “Adaptive Graph Auto-Encoder for General Data Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), vol. 44, no. 12, pp. 9725–9732, 2022.
  • Rui Zhang, Hongyuan Zhang, and Xuelong Li, “Unsupervised Feature Selection With Extended OLSDA via Embedding Nonnegative Manifold Structure”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 33, no. 5, pp. 2274–2280, 2022.
  • Hongyuan Zhang, Rui Zhang, Xuelong Li, and Yueshen Xu, “Robust Multi-View Fuzzy Clustering via Softmin”, Neurocomputing, vol. 458, pp. 47–55, 2021.
  • Rui Zhang, Xuelong Li, Qi Wang, and Hongyuan Zhang, “Autoencoder Constrained Clustering With Adaptive Neighbors”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 32, no. 1, pp. 443–449, 2021.
  • Rui Zhang, Hongyuan Zhang, and Xuelong Li, “Robust Multi-Task Learning with Flexible Manifold Constraint”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ), vol. 43, no. 6, pp. 2150–2157, 2021.
  • Rui Zhang, Xuelong Li, Hongyuan Zhang, and Feiping Nie, “Deep Fuzzy K-Means with Adaptive Loss and Entropy Regularization”, IEEE Transactions on Fuzzy Systems (T-FS), vol. 28, no. 11, pp. 2814–2824, 2020.