Publications

* denotes equal contribution.

Preprints

• Z. Zhao, Y. Liu, M. Zhao, D. Yin, Y. Yuan, L. Zhou, ``Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation”, arxiv:2308.11903.
• Z. Wang,Z. Zhao, Y. Wu, L. Zhou, D. Xu, ``Progressive Target-Styled Feature Augmentation for Unsupervised Domain Adaptation on Point Clouds”. arXiv preprint arXiv:2311.16474.
• Z. Zhao, Z. Wang, L. Wang, Y. Yuan, L. Zhou ``Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation”, arXiv preprint arXiv:2311.17325.
• Z. Wang, L. Wang, \textbf{Z. Zhao}, M. Wu, C. Lyu, H. Li, D. Cai, L. Zhou, S. Shi, Z. Tu, ``GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation”. arXiv preprint arXiv:2311.16511.
• Z. Wang, Z. Zhao, E. Guo, L. Zhou, ``Clean Label Disentangling for Medical Image Segmentation with Noisy Labels”, arXiv preprint arXiv:2311.16580.
• Z. Xu, Y. Zhang, E. Xie, Z. Zhao, Y. Guo, K.K. Wong, Z. Li, H. Zhao, ``DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model”. arXiv preprint arXiv:2310.01412.

ML-related

• Z. Zhao, L. Yang, S. Long, J. Pi, L. Zhou, J. Wang, ``Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation”, CVPR, 2023
• S. Long*, Z. Zhao*, J. Pi, S. Wang, J. Wang, ``Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers”, CVPR, 2023
• Z. Zhao*, S. Long*, J. Pi, J. Wang, L. Zhou, ``Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation”, CVPR, 2023
• Z. Wang, Z. Zhao, L. Zhou, D. Xu, X. Xing, X. Kong, ``Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation”, CVPR, 2023
• Z. Zhao, M. Zhao, Y. Liu, D. Yin, L. Zhou, ``Entropy-based Optimization on Individual and Global Predictions for Semi-Supervised Learning”, ACMMM, 2023
• S. Long*, Z. Zhao*, J. Yuan, Z. Tan, J. Liu, L. Zhou, S. Wang, J. Wang, ``Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models”, ICCV, 2023
• L. Yang, Z. Zhao, L. Qi, Y. Qiao, Y. Shi, H. Zhao, ``Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning”, ICCV, 2023
• G. Gui, Z. Zhao, L. Qi, L. Zhou, L. Wang, Y. Shi, ``Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning”, ICCV (Oral), 2023
• Y. Duan, Z. Zhao, L. Qi, L. Zhou, L. Wang, Y. Shi, ``Class Transition Tracking Based Pseudo-Rectifying Guidance for Semi-supervised Learning with Non-random Missing Labels”, ICCV, 2023
• B. Lao, S. Jaiswal, Z. Zhao, L. Lin, J. Wang, X. Sun, S-L Qin, ``Radio sources segmentation and classification with deep learning”, Astronomy and Computing, 2023
• Y. Duan, Z. Zhao, L. Qi, L. Wang, L. Zhou, Y. Shi, and Y. Gao, “MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.
• Z. Wang, Z. Zhao, L. Qi, Y. Shi, Y. Gao. “ Adaptive Weight of Unreliable Relation Module for Semi-supervised Multi-label Image Recognition. ” IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS), 2022.
• G. Guan*, Z. Zhao*, L. Qi, L. Zhou, L. Wang, and Y. Shi, “Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class”, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) (spotlight), 2022
• Z. Zhao, L. Zhou, Y. Duan, L. Wang, L. Qi, and Y. Shi, “DC-SSL: Addressing Mismatched Class Distribution in Semi-supervised Learning”, CVPR, 2022.
• Z. Zhao, L. Zhou, L. Wang, Y. Shi, Y. Gao， “LaSSL: Label-Guided Self-training for Semi-supervised Learning”, AAAI (oral), 2022.

EE-related

• C. Ying, Z. Zhao, C. Yi, Y. Shi, J. Cai, ‘‘An AoTI-Driven Joint Sampling Frequency and Access Selection Optimization for Industrial Wireless Sensor Networks’’, IEEE Transactions on Vehicular Technology, to appear, 2023.
• C. Ying, Z. Zhao, C. Yi, Y. Shi and R. Wang, ‘‘AoTI Minimization for Multi-Type Data Sampling in Industrial Wireless Sensor Networks,’’ IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Wuhan, China, Oct. 28-30, 2022.
• Z. Zhao, H. Zhang, T. An, X. Liu, B. Lao, “Pulsar Candidate Selection by An Ensemble Learning Method”, submitted to Astronomy and Computing, 2020
• Z. Zhao, T. An, B. Lao, “VLBI network simulator: an integrated simulation tool for radio astronomers”, Journal of The Korean Astronomical Society, vol. 52, no. 5, pp. 207-216, 2019.
• H. Zhang, Z. Zhao, T. An, et. al., “Pulsar candidate recognition with deep learning”, Computers and Electrical Engineering, vol. 73, pp. 1-8, January 2019.
• T. AN, S. Jaiswal, P. Mohan, Z. Zhao, B. Lao, “A Cosmic Microscope to Probe the Universe from Present to Cosmic Dawn:Dual-element Low-frequency Space VLBI Observatory”， Chinese Journal of Space Science, vol. 39, no. 2, pp. 242-249, 2019.
• Z. Zhao, S. Huang, J. Cai, “An analytical framework for IEEE 802.15.6 based wireless body area networks with instantaneous delay constraints and shadowing interruptions,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6355-6369, 2018
• Z. Zhao, S. Huang, J. Cai, “(Invited Paper) Energy efficient packet transmission strategies for wireless body area networks with rechargeable sensors,” IEEE VTC 2017-Fall, Toronto, Canada, Sept. 2017.
• C. Yi, Z. Zhao, J. Cai, R.L. Faria, “Priority-aware pricing-based capacity sharing scheme for beyond-wireless body area networks”, Computer Networks, vol. 98, pp. 29-43, Apr. 2016.
• Z. Zhao, C. Yi, J. Cai, and H. Cao, “Queueing analysis for medical packet transmission with delay-dependant packet priorities in WBANs”, WCSP’16, Oct. 13-15, 2016.
• H. Cao, J. Cai, A. S. Alfa, Z. Zhao, “Efficient resource allocation scheduling for MIMO-OFDMA-CR downlink systems”, WCSP’16, Oct. 13-15, 2016.