Xiawu Zheng （郑侠武）

About me

Xiawu Zheng is currently an associate professor in Xiamen University. He was a  postdoc researcher, working with YonghongTian, Rongrong Ji and Jie Chen in Pengcheng National Lab. He received Ph.D. (3.5-year early graduation) from  Xiamen University under the supervision of Prof. Rongrong Ji. His research interests include Model Compression, Neural Architecture Search, and Automated Machine Learning.

News

• [2024.07.11] 2 paper were accepted by ECCV2024

• [2024.04.17] 1 paper was accepted by IJCAI2024

• [2024.02.27] 4 papers were accepted by CVPR2024

• [2024.01.28] 3 papers were accepted by ICLR2024

• [2022.06.29] I was selected as the “Postdoctoral Innovative Talent Support Program” of 2022 (第5批博士后创新人才支持计划)  

Publications

Journal

1. Xiawu Zheng, Rongrong Ji, Yuhang Chen, Qiang Wang, Baochang Zhang, Qixiang Ye, Jie Chen, Feiyue Huang, Yonghong Tian. MIGO-NAS: Towards Fast and Generalizable Neural Architecture Search. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 2021.

2. Xiawu Zheng, Yang Zhang, Sirui Hong, Huixia Li, Lang Tang, Youcheng Xiong, Jin Zhou, Yan Wang, Xiaoshuai Sun, Pengfei Zhu, Chengling Wu, Rongrong Ji. Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 2021.

3. Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, Rongrong Ji, David S. Doermann, Guodong Guo. Binarized Neural Architecture Search for Efficient Object Recognition. International Journal of Computer Vision (IJCV) 2021.

4. Xiawu Zheng, Chenyi Yang, Shaokun Zhang, Yan Wang, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao, Rongrong Ji. DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning. International Journal of Computer Vision (IJCV) 2023.

5. Qinqin Zhou†, Kekai Sheng†, Xiawu Zheng†, Ke Li, Yonghong Tian, Jie Chen, Rongrong Ji. Training-free Transformer Architecture Search with Zero-cost Proxy Guided Evolution. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) , 2024.

6. Tianshuo Xu, Lijiang Li, Peng Mi, Xiawu Zheng, Fei Chao, Rongrong Ji,Qiang Shen. Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2024

Conference

1. Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, Yonghong Tian. A Unified Framework for Soft Threshold Pruning. [Code] In ICLR 2023

2. Haojia Lin, Xiawu Zheng, Lijiang Li, Fei Chao, Shanshan Wang, Yan Wang, Yonghong Tian, Rongrong Ji. Meta Architecure for Point Cloud Analysis. [Code] In CVPR 2023

3. Yuexiao Ma, Huixia Li, Xiawu Zheng, Xuefeng Xiao, Rui Wang, Shilei Wen, Xin Pan, Fei Chao, Rongrong Ji. Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective. [Code] In CVPR 2023

4. Xiawu Zheng†, Xiang Fei†, Lei Zhang†, Chenglin Wu, Fei Chao, Jianzhuang Liu, Wei Zeng, Yonghong Tian, Rongrong Ji*. Neural Architecture Search with Representation Mutual Information. In CVPR 2022

5. Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, Rongrong Ji*. Training-free Transformer Architecture Search. In CVPR 2022

6. Qinqin Zhou, Xiawu Zheng, Liujuan Cao, Bineng Zhong, Teng Xi, Gang Zhang, Errui Ding, Mingliang Xu, Rongrong Ji*. EC-DARTS: Inducing Equalized and Consistent Optimization into DARTS. In ICCV 2021

7. Huixia Li, Chenqian Yan, Shaohui Lin, Xiawu Zheng, Baochang Zhang, Fan Yang, Rongrong Ji*. PAMS: Quantized Super-Resolution via Parameterized Max Scale. ECCV, 2020.

8. Xiawu Zheng, Rongrong Ji*, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian. Rethinking Performance Estimation in Neural Architecture Search. [code] In CVPR2020.

9. Hanlin Chen, Li’an Zhuo, Baochang Zhang*, Xiawu Zheng, Jianzhuang Liu, David Doermann, Rongrong Ji. Binarized Neural Architecture Search. In AAAI2020.

10. Xiawu Zheng, Rongrong Ji*, Lang Tang, Baochang Zhang, Jianzhuang Liu, Tian Qi. Multinomial Distribution Learning for Effective Neural Architecture Search. In ICCV2019 (oral). [code]

11. Xiawu Zheng, Rongrong Ji*, Xiaoshuai Sun, Baochang Zhang, Yongjian Wu, Feiyue Huang. Towards Optimal Fine Grained Retrieval via Decorrelated Centralized Loss with Normalize-Scale layer.In AAAI2019. [code]

12. Xiawu Zheng, Rongrong Ji*, Xiaoshuai Sun, Yongjian Wu, Feiyue Huang, Yanhua Yang. Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval. In IJCAI2018.

Academic Competitions

• AutoDL 2019: the final challenge in 2019 AutoDL challenges series, Rank 1st.

• AutoCV2 2019: IMAGE + VIDEO Automated Computer Vision challenge, Rank 2st / 34 teams.

 Projects

• MetaGPT , which assign different roles to GPTs to form a collaborative software entity for complex tasks.

• XNAS is an effective, modular and flexible neural architecture search (NAS) codebase, which aims to provide a common framework and baselines for the NAS community.

• XCompression is An easy use Neural Network Compression framework.

• XBBO is an effective, modular and flexible black-box optimization (BBO) codebase, which aims to provide a common framework and benchmark for the BBO community.