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Zhun Zhong

Assistant Professor in Computer Vision, Faculty of Science

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Biography

Dr. Zhun Zhong obtained his Ph.D. degree in 2019 from Xiamen University and currently is an assistant professor at the University of Nottingham. His research focuses on computer vision, and he has published over 30 peer-reviewed papers in top conferences and journals such as CVPR, TPAMI, and NeurIPS (obtaining over 7,700 Google Scholar Citations). Dr. Zhong's proposed data augmentation method, widely utilized in the computer vision community, has obtained more than 2,800 citations and is incorporated into the renowned deep learning framework PyTorch. Furthermore, he was an area chair or senior program committee in several top conferences, including ACM MM, AAAI, and IJCAI. Dr. Zhong received the Outstanding Reviewer Award at CVPR 2020 and NeurIPS 2021. Additionally, he serves as a guest editor for IJCV. He was selected as the AI 2000 Most Influential Scholar Honorable Mention in AAAI/IJCAI in 2021 and 2022. He was included in World's Top 2% Scientists 2022 by Stanford University.

Expertise Summary

I commit to designing robust and scalable visual recognition systems for real-world applications. To achieve this goal, I mainly focus on the areas of data augmentation, unsupervised/semi- supervised learning, domain generalization, domain adaptation and novel class discovery, and investigate their advantages in visual tasks, such as object retrieval, image classification, semantic segmentation, etc.

Selected Publications

  • PU, NAN, ZHONG†, ZHUN and SEBE, NICU, 2023. A Memorizing and Generalizing Framework for Lifelong Person Re-Identification IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • LU, YAN, ZHONG, ZHUN and SHU, YUANCHAO, 2023. Multi-View Domain Adaptive Object Detection on Camera Networks In: AAAI.
  • LING, YONGGUO, ZHONG, ZHUN, CAO, DONGLIN, LUO, ZHIMING, LIN, YAOJIN, LI, SHAOZI and SEBE, NICU, 2023. Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification In: AAAI.
  • JUANJUAN WENG, ZHIMING LUO, ZHONG, ZHUN, DAZHEN LIN and LI, SHAOZI, 2023. Exploring Non-Target Knowledge for Improving Ensemble Universal Adversarial Attacks In: AAAI.

I am looking for highly-motivated Ph.D. students strarting from September 2024; I also welcome visiting students/research assistant.

I am also a guest researcher at UNITN (MHUG led by Prof. Nicu Sebe), in which we are hiring highly-motivated Postdoc/Ph.D. students/visiting students/research assistant.

Drop me an email if you are interested in the above positions. Please use the subject format [Name_Position_AppliedUniversity]. For example, [Jack_PhD_UoN], [Mike_Postdoc_UNITN].

  • PU, NAN, ZHONG†, ZHUN and SEBE, NICU, 2023. A Memorizing and Generalizing Framework for Lifelong Person Re-Identification IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • LU, YAN, ZHONG, ZHUN and SHU, YUANCHAO, 2023. Multi-View Domain Adaptive Object Detection on Camera Networks In: AAAI.
  • LING, YONGGUO, ZHONG, ZHUN, CAO, DONGLIN, LUO, ZHIMING, LIN, YAOJIN, LI, SHAOZI and SEBE, NICU, 2023. Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification In: AAAI.
  • JUANJUAN WENG, ZHIMING LUO, ZHONG, ZHUN, DAZHEN LIN and LI, SHAOZI, 2023. Exploring Non-Target Knowledge for Improving Ensemble Universal Adversarial Attacks In: AAAI.
  • WU, LINSHAN, ZHONG, ZHUN, FANG, LEYUAN, HE, XINGXIN, LIU, QIANG, MA, JIAYI and CHEN, HAO, 2023. Sparsely Annotated Semantic Segmentation With Adaptive Gaussian Mixtures In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • WANG, WEI, ZHONG, ZHUN, WANG, WEIJIE, CHEN, XI, LING, CHARLES, WANG, BOYU and SEBE, NICU, 2023. Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • PU, NAN, ZHONG†, ZHUN and SEBE, NICU, 2023. Dynamic Conceptional Contrastive Learning for Generalized Category Discovery In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • WANG, JING, LI, WENJING, LI, FANG, ZHANG, JUN, WU, ZHONGCHENG, ZHONG, ZHUN and SEBE, NICU, 2023. 100-Driver: A Large-Scale, Diverse Dataset for Distracted Driver Classification IEEE Transactions on Intelligent Transportation Systems.
  • LIU, HONG, ZHONG†, ZHUN, SEBE, NICU and SATOH, SHIN'ICHI, 2023. Mitigating robust overfitting via self-residual-calibration regularization Artificial Intelligence. 103877
  • WU, LINSHAN, FANG, LEYUAN, HE, XINGXIN, HE, MIN, MA, JIAYI and ZHONG, ZHUN, 2023. Querying Labeled for Unlabeled: Cross-Image Semantic Consistency Guided Semi-Supervised Semantic Segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • ZHONG*†, ZHUN, ZHAO*, YUYANG, LEE, GIM HEE and SEBE, NICU, 2022. Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation In: NeurIPS.
  • ZHAO, YUYANG, ZHONG, ZHUN, ZHAO, NA, SEBE, NICU and LEE, GIM HEE, 2022. Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation In: ECCV.
  • ZHANG, JICHAO, SANGINETO, ENVER, TANG, HAO, SIAROHIN, ALIAKSANDR, ZHONG, ZHUN, SEBE, NICU and WANG, WEI, 2022. 3D-Aware Semantic-Guided Generative Model for Human Synthesis In: ECCV.
  • ZHAO*, YUYANG, ZHONG*†, ZHUN, LUO, ZHIMING, LEE, GIM HEE and SEBE, NICU, 2022. Source-Free Open Compound Domain Adaptation in Semantic Segmentation IEEE Transactions on Circuits and Systems for Video Technology.
  • ZHAO, YUYANG, ZHONG, ZHUN, SEBE, NICU and LEE, GIM HEE, 2022. Novel Class Discovery in Semantic Segmentation In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • ROY, SUBHANKAR, LIU, MINGXUAN, ZHONG, ZHUN, SEBE, NICU and RICCI, ELISA, 2022. Class-incremental Novel Class Discovery In: ECCV.
  • YANG, FENGXIANG, WENG, JUANJUAN, ZHONG, ZHUN, LIU, HONG, WANG, ZHENG, LUO, ZHIMING, CAO, DONGLIN, LI, SHAOZI, SATOH, SHIN'ICHI and SEBE, NICU, 2022. Towards Robust Person Re-Identification by Defending Against Universal Attackers IEEE Transactions on Pattern Analysis and Machine Intelligence. 1-17
  • FINI, ENRICO, SANGINETO, ENVER, LATHUILIÈRE, STÉPHANE, ZHONG†, ZHUN, NABI, MOIN and RICCI, ELISA, 2021. A Unified Objective for Novel Class Discovery In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oral.
  • ZHONG*, ZHUN, FINI*, ENRICO, ROY, SUBHANKAR, LUO, ZHIMING, RICCI, ELISA and SEBE, NICU, 2021. Neighborhood Contrastive Learning for Novel Class Discovery In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10867-10875
  • ZHONG, ZHUN, ZHU, LINCHAO, LUO, ZHIMING, LI, SHAOZI, YANG, YI and SEBE, NICU, 2021. OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • YANG*, FENGXIANG, ZHONG*, ZHUN, LUO, ZHIMING, YUANZHENG, CAI, LI, SHAOZI and SEBE, NICU, 2021. Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • ZHAO*, YUYANG, ZHONG*, ZHUN, YANG, FENGXIANG, LUO, ZHIMING, LI, SHAOZI and SEBE, NICU, 2021. Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • ROY, SUBHANKAR, KRIVOSHEEV, EVGENY, ZHONG†, ZHUN, RICCI, ELISA and SEBE, NICU, 2021. Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • YANG, FENGXIANG, ZHONG, ZHUN, LIU, HONG, WANG, ZHENG, LUO, ZHIMING, LI, SHAOZI, SEBE, NICU and SATOH, SHIN’ICHI, 2021. Learning to Attack Real-World Models for Person Re-identification via Virtual-Guided MetaLearning In: AAAI.
  • ZHONG, ZHUN, ZHENG, LIANG, LUO, ZHIMING, LI, SHAOZI and YANG, YI, 2021. Learning to Adapt Invariance in Memory for Person Re-identification IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 43(8), 2723-2738
  • ZHONG, ZHUN, ZHENG, LIANG, KANG, GUOLIANG, LI, SHAOZI and YANG, YI, 2020. Random Erasing Data Augmentation In: AAAI.
  • ZHONG, ZHUN, ZHENG, LIANG, LUO, ZHIMING, LI, SHAOZI and YANG, YI, 2019. Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • ZHONG, ZHUN, ZHENG, LIANG, ZHENG, ZHEDONG, LI, SHAOZI and YANG, YI, 2019. CamStyle: A Novel Data Augmentation Method for Person Re-identification IEEE Transactions on Image Processing. 28(3), 1176-1190
  • ZHONG, ZHUN, ZHENG, LIANG, LI, SHAOZI and YANG, YI, 2018. Generalizing A Person Retrieval Model Hetero-and Homogeneously In: Proceedings of the European Conference on Computer Vision (ECCV). 172-188
  • ZHONG, ZHUN, ZHENG, LIANG, ZHENG, ZHEDONG, LI, SHAOZI and YANG, YI, 2018. Camera style adaptation for person re-identification In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5157-5166
  • ZHONG, ZHUN, ZHENG, LIANG, CAO, DONGLIN and LI, SHAOZI, 2017. Re-ranking person re-identification with k-reciprocal encoding In: Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. 3652-3661
  • ZHONG, ZHUN, LEI, MINGYI, CAO, DONGLIN, FAN, JIANPING and LI, SHAOZI, 2017. Class-specific object proposals re-ranking for object detection in automatic driving Neurocomputing. 242, 187-194
  • ZHONG, ZHUN, SU, SONGZHI, CAO, DONGLIN, LI, SHAOZI and LV, ZHIHAN, 2017. Detecting ground control points via convolutional neural network for stereo matching Multimedia Tools and Applications. 76(18), 18473-18488
  • ZHONG, ZHUN, LI, ZONGMIN, LI, RUNLIN and SUN, XIAOXIA, 2015. Unsupervised domain adaption dictionary learning for visual recognition ICIP-NoShow-PAKDD.
  • LIN, JINLIANG, ZHENG, ZHEDONG, ZHONG, ZHUN, LUO, ZHIMING, LI, SHAOZI, YANG, YI and SEBE, NICU, Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization IEEE Transactions on Image Processing (TIP).

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