Zitai Wang is a Postdoctoral Fellow at the Institute of Computing Technology, Chinese Academy of Sciences. His research interests include machine learning and data mining, with a special interest in complex metric optimization, long-tailed learning, open-set learning, and efficient learning for foundation models. He has authored or co-authored 20+ academic papers in top-tier international conferences and journals, including 10+ papers in TPAMI, IJCV, ICML, and NeurIPS, with 7 papers selected as Oral/Spotlight presentations. He also won the championship in a CVPR international competition.
He has been recognized with several prestigious honors, including CSIG Outstanding Doctoral Dissertation Award, ACM Beijing Doctoral Dissertation Award, and the CAS President Award (Excellent Prize). His research is supported by the Postdoctoral Innovative Talent Support Program, the NSFC Young Scientists Fund (Category C), the Incubation Project of the Beijing Natural Science Foundation – Haidian Original Innovation Joint Fund, the CAS Special Research Assistant Funding Program, and General Program of the Chinese Postdoctoral Science Foundation. He serves as a reviewer for CCF-A journals and conferences including TPAMI, ICML, and NeurIPS, and was recognized as a Top Reviewer for NeurIPS.
Homepage: https://wang22ti.github.io/
Google scholar: https://scholar.google.com/citations?user=45qZ_LcAAAAJ
2024.07—Present Postdoctoral Fellow, Institute of Computing Technology, Chinese Academy of Sciences
2019.09—2024.06 Doctoral Student, Institute of Information Engineering, Chinese Academy of Sciences
2015.09—2019.06 Undergraduate Student, School of Computer and Information Technology, Beijing Jiaotong University
Machine learning and data mining, with a special interest in complex metric optimization, long-tailed learning, open-set learning, and efficient learning for foundation models
[CVPR 26] Zhikang Xu, Qianqian Xu, Zitai Wang, Cong Hua, Sicong Li, Zhiyong Yang, and Qingming Huang. Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026. (Accepted)
[CVPR 26] Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, Longtao Huang, and Qingming Huang. Making Training-Free Diffusion Segmentors Scale with the Generative Power. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026. (Accepted)
[AAAI 26] Difu feng, Qianqian Xu, Zitai Wang, Cong Hua, Zhiyong Yang, and Qingming Huang. Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation. AAAI Conference on Artificial Intelligence (AAAI), 2026. (Accepted)
[TPAMI 25] Zitai Wang, Qianqian Xu, Zhiyong Yang, Zhikang Xu, Linchao Zhang, Xiaochun Cao, and Qingming Huang. A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 48(1): 639-656, Jan. 2026.
[TPAMI 25] Zhiyong Yang, Qianqian Xu, Sicong Li, Zitai Wang, Xiaochun Cao, and Qingming Huang. DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025. (Accepted, DOI: 10.1109/TPAMI.2025.3647124)
[ICML 25] Guanghui Wang, Zhiyong Yang, Zitai Wang, Shi Wang, Qianqian Xu, and Qingming Huang. ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via α-β-Divergence. International Conference on Machine Learning (ICML), 65167-65212, 2025. (Oral, 1.0%)
[ICML 25] Cong Hua, Qianqian Xu, Zhiyong Yang, Zitai Wang, Shilong Bao, and Qingming Huang. OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning. International Conference on Machine Learning (ICML), 24975-25020, 2025.
[ICML 25] Sicong Li, Qianqian Xu, Zhiyong Yang, Zitai Wang, Linchao Zhang, Xiaochun Cao, and Qingming Huang. Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification. International Conference on Machine Learning (ICML), 36624-36651, 2025.
[AAAI 25] Yuchen Sun, Qianqian Xu, Zitai Wang, Zhiyong Yang, and Junwei He. EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion. AAAI Conference on Artificial Intelligence (AAAI), 12613-12621, 2025.
[IJCV 24] Zitai Wang, Qianqian Xu, Zhiyong Yang, Peisong Wen, Yuan He, Xiaochun Cao, and Qingming Huang. Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification. International Journal of Computer Vision (IJCV), 133(1): 211-253, Jan. 2025.
[ICML 24] Zhiyong Yang, Qianqian Xu, Zitai Wang, Sicong Li, Boyu Han, Shilong Bao, Xiaochun Cao, and Qingming Huang. Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition. International Conference on Machine Learning (ICML), 56624-56664, 2024.
[NeurIPS 24] Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, and Qingming Huang. Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features. Annual Conference on Neural Information Processing Systems (NeurIPS), 55141-55177, 2024. (Spotlight, 2.1%)
[NeurIPS 24] Benyuan Meng, Qianqian Xu, Zitai Wang, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques. Annual Conference on Neural Information Processing Systems (NeurIPS), 18910-18939, 2024.
[AAAI 24] Junwei He, Qianqian Xu, Yangbangyan Jiang, Zitai Wang, and Qingming Huang. ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection. AAAI Conference on Artificial Intelligence (AAAI), 8481-8489, 2024.
[ACM MM 24] Junwei He, Qianqian Xu, Yangbangyan Jiang, Zitai Wang, Yuchen Sun, and Qingming Huang. HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection. ACM Conference on Multimedia (ACM MM), 1544-1553, 2024.
[NeurIPS 23] Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. Annual Conference on Neural Information Processing Systems (NeurIPS), 48417-48430, 2023. (Spotlight, 3.1%)
[ACM MM 23] Yuchen Sun, Qianqian Xu, Zitai Wang, and Qingming Huang. When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learning. ACM Conference on Multimedia (ACM MM), 1515-1526, 2023. (Oral, 5.4%)
[TPAMI 22] Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. Optimizing Partial Area Under the Top-k Curve: Theory and Practice. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, IF: 18.6), 45(4): 5053-5069, Apr. 2023.
[NeurIPS 22] Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. OpenAUC: Towards AUC-Oriented Open-Set Recognition. Annual Conference on Neural Information Processing Systems (NeurIPS), 25033-25045, 2022. (Spotlight, 5.4%)
[ACM MM 22] Zitai Wang, Qianqian Xu, Ke Ma, Xiaochun Cao, and Qingming Huang. Confederated Learning: Going Beyond Centralization. ACM Conference on Multimedia (ACM MM), 2939-2947, 2022. (Oral, 5.6%)
[ACM MM 21] Zitai Wang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. Implicit feedbacks are not always favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions. ACM Conference on Multimedia (ACM MM), 3070-3078, 2021.
[ACM MM 19] Zitai Wang, Qianqian Xu, Ke Ma, Yangbangyan Jiang, Xiaochun Cao, and Qingming Huang. Adversarial Preference Learning with Pairwise Comparisons. ACM Conference on Multimedia (ACM MM), 656-664, 2019. (Oral, 9.4%)
