Neural Forest Learning. - Yun-Hao Cao; Jianxin Wu 001 (CoRR, 2019)

Rethinking the Route Towards Weakly Supervised Object Localization. - Chen-Lin Zhang; Yun-Hao Cao; Jianxin Wu 001 (CVPR, 2020)

Rethinking Self-Supervised Learning: Small is Beautiful. - Yun-Hao Cao; Jianxin Wu 001 (CoRR, 2021)

Neural random subspace. - Yun-Hao Cao; Jianxin Wu 001; Hanchen Wang 002; Joan Lasenby (Pattern Recognit., 2021)

A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications. - Yun-Hao Cao; Jianxin Wu 001 (AAAI, 2022)

Synergistic Self-supervised and Quantization Learning. - Yun-Hao Cao; Peiqin Sun; Yechang Huang; Jianxin Wu 001; Shuchang Zhou 001 (ECCV (30), 2022)

Training Vision Transformers with only 2040 Images. - Yun-Hao Cao; Hao Yu; Jianxin Wu 001 (ECCV (25), 2022)

Worst Case Matters for Few-Shot Recognition. - Minghao Fu 001; Yun-Hao Cao; Jianxin Wu 001 (ECCV (20), 2022)

Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning. - Yun-Hao Cao; Peiqin Sun; Shuchang Zhou 001 (CVPR, 2023)

Towards Better Vision-Inspired Vision-Language Models. - Yun-Hao Cao; Kaixiang Ji; Ziyuan Huang; Chuanyang Zheng; Jiajia Liu 002; Jian Wang 108; Jingdong Chen; Ming Yang 007 (CVPR, 2024)

On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning. - Yun-Hao Cao; Jianxin Wu 001 (CoRR, 2024)

Tobias: A Random CNN Sees Objects. - Yun-Hao Cao; Jianxin Wu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Random Subspace Sampling for Classification with Missing Data. - Yun-Hao Cao; Jian-Xin Wu (J. Comput. Sci. Technol., 2024)

EFPC: Towards Efficient and Flexible Prompt Compression. - Yun-Hao Cao; Yangsong Wang; Shuzheng Hao; Zhenxing Li; Chengjun Zhan; Sichao Liu; Yi-Qi Hu (CoRR, 2025)