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Pengxu Wei (魏朋旭)

HCP Lab, Sun Yat-sen University

About Me

I am an associate professor at the at the School of Computer Science and Engineering, Sun Yat-sen University (SYSU), where I work in HCP Lab (Human-Cyber-Physical Intelligence Integration Lab) supervised by Prof. Liang Lin. Before that, I received the Ph.D in Computer Science from University of the Chinese Academy of Sciences in 2018, advised by Prof. Jianbin Jiao and Prof. Qixiang Ye. I obtained the B.Eng. from China University of Mining and Technology, Beijing, advised by Prof. Feng Yang.

Research interest: My general research interest is Towards Transferable, Robust and Reliable Model Learning for computer vision tasks. Recently, I specifically focus on

  • 1) high-level vision: weakly-supervised object detection, unsupervised domain adaptation, robust object detection, adversarial attack and defense.
  • 2) low-level vision: real-world image Super-Resolution (e.g., real-world SR benchmarks, single image real-world SR, unsupervised domain adaptation real-world SR, robust real-world SR).
  • 3) text-to-image generation: controllable text-to-image/video generation.

    • News

    • [Oct 2023] One paper was selected as Best Paper candidate for PRCV 2023.
    • [Jun 2023] Two papers are accepted for ICCV 2023.
    • [Apr 2023] We have released a toolbox (HCP-Diffusion) for Stable Diffusion models based on Diffusers. It facilitates flexible configurations and component support for training, in comparison with webui and sd-scripts.
    • [Mar 2023] Three papers are accepted for CVPR 2023.
    • [Feb 2023] One paper on real-world SR is accepted for TIP 2023.
    • [Feb 2023] One paper on scene graph to image synthesis is accepted for AAAI 2023.
    • [Jul 2022] One paper on adversarially-robust object detector is accepted as ECCV 2022 oral paper (2.7% of submitted papers). [Project]
    • [Mar 2022] One paper on real-world SR is accepted as CVPR 2022 oral paper (4.2% of submitted papers). [Project]
    • [Aug 2021] IEEE CASS Seasonal School on New Trends of Visual and Language Understanding was held online on August 20-22, 2021.
    • [Aug 2020] AIM 2020 Real Image Super-Resolution Challenge starts on May 8, 2020 and ends on July 17, 2021. Congratulations to the Challenge Winners! [Awards]
    • [Aug 2020] A large-scale diverse real-world image super-resolution dataset, DRealSR, is released. [Link]

    Selected Publications

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    Towards Real-World Burst Image Super-Resolution: Benchmark and Method

    Pengxu Wei, Yujing Sun, Xingbei Guo, Chang Liu, Guanbin Li, Jie Chen, Xiangyang Ji, Liang Lin

    ICCV 2023 [Project, Dataset&Code, Paper]

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    Masked Images Are Counterfactual Samples for Robust Fine-tuning

    Yao Xiao, Ziyi Tang, Pengxu Wei*, Cong Liu, Liang Lin

    CVPR 2023 [Project, Code, Paper]

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    Taylor Neural Network for Real-World Image Super-Resolution

    Pengxu Wei, Ziwei Xie, Guanbin Li, Liang Lin*

    TIP 2023 [Code (coming soon), Paper]

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    Scene Graph to Image Synthesis via Knowledge Consensus

    Yang Wu, Pengxu Wei*, Liang Lin

    AAAI 2023 [Code (coming soon), Paper]

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    Adversarially-Aware Robust Object Detector

    Ziyi Dong, Pengxu Wei*, Liang Lin

    ECCV 2022 (oral) [Project, Code, Paper Supplementary]

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    Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

    Xiaoqian Xu, Pengxu Wei*, Weikai Chen, Yang Liu, Liang Lin and Guanbin Li

    CVPR 2022 (oral) [Project, Code, Paper]

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    Cross-Domain Action Recognition via Prototypical Graph Alignment

    Binbin Yang, Junhao Zhong, Pengxu Wei*, Dongyu Zhang and Liang Lin

    ICME 2022 (oral) [Paper]

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    Robust Real-World Image Super-Resolution against Adversarial Attacks

    Jiutao Yue, Haofeng Li, Pengxu Wei*, Guanbin Li and Liang Lin

    ACM Multimedia, 2021. [Paper, BibTex]

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    Component Divide-and-Conquer for Real-World Image Super-Resolution

    Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixiang Ye, Wangmeng Zuo and Liang Lin

    ECCV, 2020. [Paper, Project, Code, BibTex]

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    AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

    Pengxu Wei*, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, et al.

    ECCV workshop, 2020. [Paper, BibTex]

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    Deep CockTail Networks: A Universal Framework for Visual Multi-source Domain Adaptation

    Ziliang Chen, Pengxu Wei*, Jingyu Zhuang, Guanbin Li and Liang Lin

    IJCV, 2021. [Paper, BibTex]

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    Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation

    Changxin Huang, Ronghui Zhang, Meizi Ouyang, Pengxu Wei*, Junfan Lin, Jiang Su and Liang Lin

    IEEE TNNLS, 2021. [Paper, BibTex]

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    Deductive Learning for Weakly-Supervised 3D Human Pose Estimation via Uncalibrated Cameras

    Xipeng Chen, Pengxu Wei* and Liang Lin

    AAAI, 2021. [Paper, BibTex]

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    Min-Entropy Latent Model for Weakly Supervised Object Detection

    Fang Wan, Pengxu Wei, Zhenjun Han, Jianbin Jiao and Qixiang Ye

    CVPR 2018 [Paper]; IEEE T-PAMI, 2019 [Paper, Project&Code]

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    3D Human Pose Machines with Self-Supervised Learning

    Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, Pengxu Wei

    IEEE T-PAMI 2020 [Paper] BibTex]

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    Graph-Convolved Factorization Machines for Personalized Recommendation

    Yongsen Zheng, Pengxu Wei*, Ziliang Chen, Yang Cao and Liang Lin

    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. [Paper, BibTex]

    Selected Projects

    A Near-Optimal Generative Learning Scheme for Neural Energy-Based Models

    Yang Wu, Xu cai, Pengxu Wei, Liang Lin.

    Real-World Image Super-Resolution Challenge (AIM 2020 in conjunction with ECCV 2020) [AIM2020 webpage]

    We provide high/low resolution image pairs of 3 scales captured by 5 different DSLR cameras through zooming lens. The challenging contains 3 tracks, one track per scale. The detail of the dataset is provided in Table 1. For the convergence of training, images in the trainset are cropped into fixed size patches using sliding window after careful alignment for image pairs. [Chanllenge Page]

    Organizers: Pengxu Wei (Sun Yat-Sen University), Hannan Lu (Harbin Institute of Technology), Wangmeng Zuo (Harbin Institute of Technology), Radu Timofte (ETH Zurich)

    IEEE CASS Seasonal School on New Trends of Visual and Language Understanding (NT-VLU) [Website, Video]

    To organize the IEEE CASS Seasonal School on NT-VLU, we invited several top-tier researchers to present their works and arrange two panels to discuss the new trends of NT-VLU. This seasonal school aimed to promote the frontier of language and vision research and to provide an ambiance for fruitful exchange of ideas and discussion of advances, challenges, and trends of this area in the near future. [Website]

    Spearkers: Jiebo Luo (University of Rochester), Cees G.M. Snoek (University of Amsterdam), Wen-Huang Cheng (National Yang Ming Chiao Tung University), Hanwang Zhang (Nanyang Technological University), Michael Kampffmeyer (UiT the Arctic University of Norway), Dan Xu (Hong Kong University of Sciences and Technology), Pengfei Liu (Carnegie Mellon University)

    Organizers: Liang Lin(Sun Yat-Sen University), Wangmeng Zuo (Harbin Institute of Technology), Xiaodan Liang(Sun Yat-Sen University), Yunchao Wei (Beijing Jiaotong University), Pengxu Wei (Sun Yat-Sen University), Guanbin Li(Sun Yat-Sen University)