Learning to Impute: A General Framework for Semi-supervised Learning. - Wei-Hong Li; Chuan-Sheng Foo; Hakan Bilen (CoRR, 2019)

Knowledge Distillation for Multi-task Learning. - Wei-Hong Li; Hakan Bilen (ECCV Workshops (6), 2020)

Universal Representation Learning from Multiple Domains for Few-shot Classification. - Wei-Hong Li; Xialei Liu; Hakan Bilen (ICCV, 2021)

Network differentiation: A computational method of pathogenesis diagnosis in traditional Chinese medicine based on systems science. - Qiang Xu; Qiang Guo; Chun-Xia Wang; Song Zhang; Chuanbiao Wen; Tao Sun; Wei Peng; Jun Chen; Wei-Hong Li (Artif. Intell. Medicine, 2021)

Improving Task Adaptation for Cross-domain Few-shot Learning. - Wei-Hong Li; Xialei Liu; Hakan Bilen (CoRR, 2021)

Cross-domain Few-shot Learning with Task-specific Adapters. - Wei-Hong Li; Xialei Liu; Hakan Bilen (CVPR, 2022)

Learning Multiple Dense Prediction Tasks from Partially Annotated Data. - Wei-Hong Li; Xialei Liu; Hakan Bilen (CVPR, 2022)

Learning Relation Models to Detect Important People in Still Images. - Yukun Qiu; Fa-Ting Hong; Wei-Hong Li; Wei-Shi Zheng 001 (IEEE Trans. Multim., 2023)

Multi-task Learning with 3D-Aware Regularization. - Wei-Hong Li; Steven McDonagh 001; Ales Leonardis; Hakan Bilen (ICLR, 2024)

Universal Representations: A Unified Look at Multiple Task and Domain Learning. - Wei-Hong Li; Xialei Liu; Hakan Bilen (Int. J. Comput. Vis., 2024)

UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines. - Chen Tang; Xinzhu Ma; Encheng Su; Xiufeng Song; Xiaohong Liu 001; Wei-Hong Li; Lei Bai 001; Wanli Ouyang; Xiangyu Yue 001 (CVPR, 2025)