Researcher & 3DTrans Team Leader, Shanghai AI Laboratory.
Office: Level 12, L1, Longwen Road No. 129, Xuhui District, Shanghai, China
Email: zhangbo [at] pjlab.org.cn & bo.zhangzx [at] gmail.com
Academic activities: Reviewer at CVPR/ECCV/ICCV/ACM-MM & TIP/TGRS/T-CSVT/T-MM/T-NNLS
Bo Zhang received the Ph.D. degree in electronic engineering from Fudan University, in 2022. His research is focused on 3D perception-reasoning task, 2D/3D pre-training, 2D/3D continuous learning, 2D/3D few-shot learning. His research outcomes (perception-reasoning task) have some impacts on industrial applications like airport checkpoint security perceptual recognition and localization of concealed or dangerous objects. His work has led to many awards, including Shanghai Rising Star awarded by the Shanghai Municipal Commission of Science and Technology, the National Scholarship 2021 China Award, the 2019 Excellent Doctoral Scholarship of Fudan University Award, and various awards from VALSE China and Shanghai Government.
He is currently a Researcher with Shanghai AI Laboratory, working on developing many major open-source projects including: 1) Open-source codebase of continuous learning/pre-training (3DTrans Code Link); 2) Open-source perception-reasoning codebase for multi-modal structured-knowledge learning
We are hiring: Our team aims to broaden the boundaries of general perception-reasoning task, trying to find unified-yet-structured representations that can be generalized across different domains and scenarios under different modalities and tasks. Meanwhile, we are working on extracting knowledge-rich data from different domains. We are searching for highly self-motivated interns. If you are interested in our research tpoic, do not hesitate to contact me.
- Sep 24, 2023, StructChart, our research on visual chart, has been released (arXiv paper), where we will release the SimChart9K dataset powered by LLM. By the proposed SimChart9K, we observe that StructChart continuously improves the chart perception performance as more simulated charts are used for pre-training.
- Sep 23, 2023, SPOT, showing a promising and scalable 3D pre-training on autonomous driving, has been released (See our paper for more details, arXiv paper).
- Sep 22, 2023, One paper entitled “AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset” is accepted by NeurIPS-2023.
- Aug 16, 2023, One paper about cross-domain background-fouced alignment “Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors” is accepted by TIP.
- Jul 20, 2023, One paper entitled “SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification” is accepted by ACM MM-2023.
- May 25, 2023, AD-PT, our research on 3D point-cloud pre-training, has been released (Code, arXiv paper).
- Apr 10, 2023, One paper entitled “Performance-aware Approximation of Global Channel Pruning for Multitask CNNs” is accepted for publication in T-PAMI.
- Mar 05, 2023, Bo Zhang started to work on exploring the general perception-reasoning task, by leveraging the off-the-shelf foundation LLM models powered by extracting structured knowledge-rich data.
- Mar 8, 2023, Three papers are accepted by CVPR-2023: Uni3D, Bi3D, GDP.
- Dec 20, 2022, One paper entitled “A Closer Look at Few-Shot 3D Point Cloud Classification” is accepted for publication in IJCV.
- Jun 15, 2022, Bo Zhang received the Ph.D. degree in electronic engineering from Fudan University.
- ^ refers to the corresponding author (project lead)
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset. Jiakang Yuan, Bo Zhang^, Xiangchao Yan, Tao Chen, Botian Shi, Yikang LI, Yu Qiao. Published in NeurIPS-2023. (Code, CCF A)
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors. Yancheng Cai, Bo Zhang, Baopu Li, Tao Chen, Hongliang Yan, and Jiahao Xu. Published in TIP. (Code will be available, CCF A)
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification. Siyuan Huang, Bo Zhang^, Botian Shi, Peng Gao, Yikang Li, and Hongsheng Li. Published in ACM-MM-2023. (Code, CCF A)
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs. Hancheng Ye, Bo Zhang, Tao Chen, Jiayuan Fan, and Bin Wang. Published in T-PAMI. (Code, CCF A)
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection. Bo Zhang, Jiakang Yuan, Botian Shi, Tao Chen, Yikang LI, Yu Qiao. Published in CVPR-2023. (Code, CCF A)
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection. Jiakang Yuan, Bo Zhang^, Xiangchao Yan, Tao Chen, Botian Shi, Yikang LI, Yu Qiao. Published in CVPR-2023. (Code, CCF A)
Generative Diffusion Prior for Unified Image Restoration and Enhancement. Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, Bo Dai. Published in CVPR-2023. (Code, CCF A)
A Closer Look at Few-Shot 3D Point Cloud Classification. C Ye, H Zhu, B Zhang, T Chen. Published in IJCV. (CCF A)
Learning cross-image object semantic relation in transformer for few-shot fine-grained image classification. B Zhang, J Yuan, B Li, T Chen, J Fan, B Shi. Published in ACM-MM-2022. (Code, CCF A)
Sample-centric feature generation for semi-supervised few-shot learning. B Zhang, H Ye, G Yu, B Wang, Y Wu, J Fan, T Chen. Published in TIP. (Code, CCF A)
Joint distribution alignment via adversarial learning for domain adaptive object detection. B Zhang, T Chen, B Wang, R Li. Published in TMM. (Code, Tsinghua-A, Accept without any changes during the first round)
Object-aware long-short-range spatial alignment for few-shot fine-grained image classification. Y Wu, B Zhang, G Yu, W Zhang, B Wang, T Chen, J Fan. Published in ACM-MM-2021. (CCF A)
During the period of pursuing a Ph.D degree, Bo Zhang was focused on studying domain adaptive 2D object detection or semantic segmentation models, and has deep research and practical experience for the model adaptation/transfer task.