Zelin Peng
Short Bio
I am a third-year Ph.D. student at Shanghai Jiao Tong University (SJTU), supervised by Prof. Wei Shen. My research interests include parameter-efficient learning, semantic segmentation, and medical image analysis.
Recently, I am working on multi-modal learning and medical image segmentation.
Education
- 2022.9 - Now Ph.D. in the MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, supervised by Prof. Wei Shen.
- 2019.9 - 2022.6 M.E. in the School of Artificial Intelligence, Xidian University, supervised by Prof. Xiangrong Zhang.
- 2015.9 - 2019.6 B.E. in the School of Artificial Intelligence, Xidian University.
News
[2024.08] One collaborative paper is accepted by BIBM.
[2024.07] One collaborative paper is accepted by MICCAI.
[2024.04] One collaborative paper is accepted by TNNLS.
[2024.03] One paper is accepted by CVPR.
[2023.12] One collaborative paper is accepted by AAAI.
[2023.12] One paper is accepted by AAAI.
[2023.07] One paper is accepted by ICCV.
[2023.03] One collaborative paper is accepted by TPAMI.
[2023.01] One collaborative paper is accepted by ISPRS.
[2022.07] One collaborative paper is accepted by IJCAI.
[2021.07] One paper is accepted by ACMMM.
Selected Publications
[1] Z. Peng, Z. Xu, Z. Zeng, L. Xie, Q. Tian, W. Shen, “Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (CCF A).
[2] Z. Peng, Z. Xu, Z. Zeng, X. Yang, W. Shen, “Sam-parser: Fine-tuning sam efficiently by parameter space reconstruction”, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), (CCF A).
[3] Z. Peng, G. Wang, L. Xie, D. Jiang, W. Shen, Q. Tian, “Usage: A unified seed area generation paradigm for weakly supervised semantic segmentation”, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (CCF A).
[4] W. Shen, Z. Peng, X. Wang, H. Wang, J. Cen, D. Jiang, L. Xie, X. Yang, Q. Tian, “A survey on label-efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction”, IEEE transactions on pattern analysis and machine intelligence (TPAMI), (SCI Q1 Top, IF=20.8).
[5] X. Zhang, Z. Peng, P. Zhu, T. Zhang, C. Li, H. Zhou, L. Jiao, “Adaptive affinity loss and erroneous pseudo-label refinement for weakly supervised semantic segmentation”, Proceedings of the 29th ACM international conference on multimedia (ACMMM), (CCF A).
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