标签非完备下的机器学习处理的是在标签分布不均或标签噪声等显著缺陷的情况下进行的有效模型训练，目标是在数据质量较低的情况下仍能实现准确的预测。可通过引入更有效的标签处理和集成策略，提升了模型在标签非完备数据上的表现。包括长尾视觉识别、类别不平衡学习、标签噪声学习、弱监督学习、异构数据学习等子方向。

相关成果：

• 长尾视觉识别：

  [1] Decision Boundary-aware Generation for Long-tailed Learning, Jiacheng Yang, Ruichi Zhang, Chikai Shang, Mengke Li, Xinyi Shang, Junlong Gao, Yonggang Zhang, and Yang Lu*, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Colorado, USA, June 3-7, 2026. (CCF-A)

  [2] CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning, Ruichi Zhang, Chikai Shang, Jiacheng Yang, Mengke Li, Yang Zhou, Junlong Gao, and Yang Lu*, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Colorado, USA, June 3-7, 2026. (CCF-A)

  [3] Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition, Mengke Li, Ye Liu, Yang Lu, Yiqun Zhang, Yiu-ming Cheung, and Hui Huang, Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 9-15, 2024. (CCF-A)

  [4] CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data, Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu, Yuan Xie, and Yanyun Qu, AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, February 20–27, 2024. (CCF-A)

  [5] Feature Fusion from Head to Tail for Long-Tailed Visual Recognition, Mengke Li, Zhikai Hu, Yang Lu, Weichao Lan, Yiu-ming Cheung, and Hui Huang, AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, February 20–27, 2024. (CCF-A)

  [6]  Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features, Xinyi Shang, Yang Lu*, Gang Huang, and Hanzi Wang, International Joint Conference on Artificial Intelligence (IJCAI), pp.2218-2224, Vienna, Austria, July 23-29, 2022. (CCF-A)

  [7] Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment, Mengke Li, Yiu-ming Cheung, and Yang Lu, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.6929-6938 , New Orleans, Louisiana, June 21–24, 2022. (CCF-A)

  [8] Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation, Yan Jin, Mengke Li, Yang Lu*, Yiu-ming Cheung, and Hanzi Wang, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, June 18–22, 2023. (CCF-A)

• 类别不平衡学习：

  [1] Dynamically Anchored Prompting for Task-Imbalanced Continual Learning, Chenxing Hong, Yan Jin, Zhiqi Kang, Yizhou Chen, Mengke Li, Yang Lu*, and Hanzi Wang, International Joint Conference on Artificial Intelligence (IJCAI), Jeju, Korea, August 3-9, 2024. (CCF-A)
  

  [2] MOOD: Leveraging Out-of-Distribution Data to Enhance Imbalanced Semi-Supervised Learning, Yang Lu, Xiaolin Huang, Yizhou Chen, Mengke Li, Yan Yan, Chen Gong, and Hanzi Wang, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 31, no. 9, pp. 3525-3539, 2020. (JCR 1区 / CCF-B)

  [3] Self-Adaptive Multi-Prototype-based Competitive Learning Approach: A k-means-type Algorithm for Imbalanced Data Clustering, Yang Lu, Yiu-ming Cheung, and Yuan Yan Tang, IEEE Transactions on Cybernetics (TCYB), vol. 51, no. 3, pp. 1598-1612, 2021. (JCR 1区 / CCF-B)

  [4] Adaptive Chunk-based Dynamic Weighted Majority for Imbalanced Data Streams with Concept Drift, Yang Lu, Yiu-ming Cheung, and Yuan Yan Tang, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 31, no. 8, pp. 2764-2778, 2020. (JCR 1区 / CCF-B)

  [5] Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem, Yang Lu, Yiu-ming Cheung, and Yuan Yan Tang, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 31, no. 9, pp. 3525-3539, 2020. (JCR 1区 / CCF-B)

  [6] Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift, Yang Lu, Yiu-ming Cheung, and Yuan Yan Tang, International Joint Conference on Artificial Intelligence (IJCAI), pp. 2393-2399, Melbourne, Australia, August 19-25, 2017. (CCF-A)

• 标签噪声学习：

  [1] Unlocker: Disentangle the Deadlock of Learning between Label-noisy and Long-tailed Data, Chen Shu, Hongjun Xu, Ruichi Zhang, Mengke Li, Yonggang Zhang, Yang Lu*, Bo Han, Yiu-ming Cheung, and Hanzi Wang, Advances in Neural Information Processing Systems (NeurIPS), San Diego, USA, December 2-7, 2025. (CCF-A)

  [2] GradToken: Decoupling Tokens with Class-aware Gradient for Visual Explanation of Transformer Network, Lin Cheng, Yanjie Liang, Yang Lu*, and Yiu-ming Cheung, Neural Networks (NN), vol. 181, 106837, 2025. (JCR 1区 / CCF-B)

  [3] Federated Learning with Extremely Noisy Clients via Negative Distillation, Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, and Hanzi Wang, AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, February 20–27, 2024. (CCF-A)

  [4] Label-Noise Learning with Intrinsically Long-Tailed Data, Yang Lu, Yiliang Zhang, Bo Han, Yiu-ming Cheung, and Hanzi Wang, IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, October 2–6, 2023. (CCF-A)

  [5] Drop Loss for Person Attribute Recognition with Imbalanced Noisy-Labeled Samples, Yan Yan, Youze Xu, Jing-Hao Xue, Yang Lu, Hanzi Wang, and Wentao Zhu, IEEE Transactions on Cybernetics (TCYB), vol. 53, no. 11, pp. 7071-7084, 2023. (JCR 1区 / CCF-B)

  [6] Small-Vote Sample Selection for Label-Noise Learning, Youze Xu, Yan Yan, Jing-hao Xue, Yang Lu, and Hanzi Wang, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 729-744, Bilbao, Spain, September 13–17, 2021. (CCF-B)

• 弱监督学习：

  [1] SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning, Hezhao Liu, Jiacheng Yang, Junlong Gao, Mengke Li, Yiqun Zhang, Shreyank N Gowda, and Yang Lu*, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Colorado, USA, June 3-7, 2026. (CCF-A)

  [2] FATE: A Prompt-Tuning-Based Semi-Supervised Learning Framework for Extremely Limited Labeled Data, Hezhao Liu, Mengke Li, Yiqun Zhang, Shreyank N Gowda, Yang Lu*, Chen Gong, and Hanzi Wang, ACM Multimedia (MM), Dublin, Ireland, October 27 - 31, 2025. (CCF-A)

  [3] DTSNet: A Denoising Teacher-Student Network with Reverse Distillation for Anomaly Detection, Taixiang Lin, Shuyuan Lin, Yanjie Liang, Rong Chen, and Yang Lu, IEEE International Conference on Multimedia and Expo (ICME), Nantes, France, pp. 1-6, 2025. (CCF-B)

  [4] Relationship Prompt Learning is Enough for Open-Vocabulary Semantic Segmentation, Jiahao Li, Yanyun Qu, Yuan Xie, and Yang Lu, Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 9-15, 2024. (CCF-A)

• 异构数据学习:

  [1] PRO-VPT: Distribution-Adaptive Visual Prompt Tuning via Prompt Relocation, Chikai Shang, Mengke Li, Yiqun Zhang, Zhen Chen, Jinlin Wu, Fangqing Gu, Yang Lu*, and Yiu-ming Cheung, IEEE/CVF International Conference on Computer Vision (ICCV), Honolulu, Hawaii, October 19 - 23, 2025. (CCF-A)

  [2] A Unified Multi-Domain Face Normalization Framework for Cross-domain Prototype Learning and Heterogeneous Face Recognition, Meng Pang, Wenjun Zhang, Yang Lu, Yiu-ming Cheung, and Nanrun Zhou, IEEE Transactions on Information Forensics and Security (TIFS), 2025. (JCR 1区/ CCF-A)

  [3] MaskViM: Domain Generalized Semantic Segmentation with State Space Models, Jiahao Li, Yang Lu, Yuan Xie, and Yanyun Qu, AAAI Conference on Artificial Intelligence (AAAI), Pennsylvania, USA, February 25 - March 4, 2025. (CCF-A)

  [4] Novel Category Discovery with X-Agent Attention for Open-Vocabulary Semantic Segmentation, Jiahao Li, Yang Lu, Yachao Zhang, Fangyong Wang, Yuan Xie, and Yanyun Qu, ACM Multimedia (MM), Dublin, Ireland, October 27 - 31, 2025. (CCF-A)

  [5] NeurIPT: Foundation Model for Neural Interfaces, Zitao Fang, Chenxuan Li, Hongting Zhou, Shuyang Yu, Guodong Du, Ashwaq Qasem, Yang Lu, Jing Li, Junsong Zhang, and Sim Kuan Goh, Advances in Neural Information Processing Systems (NeurIPS), San Diego, USA, December 2-7, 2025. (CCF-A)

  [6] Frequency Domain Nuances Mining for Visible-Infrared Person Re-identification, Yukang Zhang, Yang Lu, Yan Yan, Hanzi Wang, and Xuelong Li, IEEE Transactions on Information Forensics and Security (TIFS), 2025. (JCR 1区/ CCF-A)

  [7] CGATracker: Correlation-Aware Graph Alignment for Referring Multi-Object Tracking, Siping Zhuang, Guangyao Li, Qiangqiang Wu, Yang Lu, Hai-Miao Hu, and Hanzi Wang, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), pp. 11337-11349, 2025. (JCR 1区 / CCF-B)

  [8] Transitive Vision-Language Prompt Learning for Domain Generalization, Liyuan Wang, Yan Jin, Zhen Chen, Jinlin Wu, Mengke Li, Yang Lu*, and Hanzi Wang, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2025.