My name is Zixiang Zhao (赵子祥, pronounced ‘Tzu-Hsiang Chao’). I am currently a PhD student in School of Mathematics and Statistics, Xi’an Jiaotong University, supervised by Prof. Jiangshe Zhang.

I am now a visiting Ph.D. student in the great Computer Vision Lab, ETH Zürich, Switzerland, supervised by Prof. Luc Van Gool and working closely with Prof. Radu Timofte, Dr. Yulun Zhang and Dr. Kai Zhang.

I have been fortunate enough to work as a Research Assistant at Visual Computing Group, Harvard University, supervised by Prof. Hanspeter Pfister and work closely with Dr. Zudi Lin.

I also work closely with Prof. Deyu Meng and Assoc. Prof. Shuang Xu in Xi’an Jiaotong University.

My research lies at low-level computer vision and model-based image processing, and my current research is multi-modal image fusion and restoration.

I’m open to any kinds of collaboration. Please feel free to contact me directly through email.

Email:
zixiangzhao@stu.xjtu.edu.cn
zixiang.zhao@hotmail.com


CURRENT RESEARCH

  1. Low-level vision (especially in Feature Separation and Information Fusion)
    • Image Fusion (e.g., infrared and visible, remote sensing, multi-focus images)
    • Multi-modal Image Restoration (e.g., RGB guided depth super-resolution, multi-spectral imaging)
    • Image Enhancement
  2. Computer Vision
    • Generative Models (e.g., generative adversarial network, denoising diffusion model)
    • Domain Adaption
  3. Machine Learning
    • Self-supervised learning
    • Clustering

NEWS

  • [HOT] Two papers are accepted by ICML 2024 (One first-authored paper on vision-language-based image fusion and the other on residual binarization for image super-resolution).
  • [HOT] One first-authored paper on multi-modality image fusion is accepted by CVPR 2024.
  • [2024-05] One paper on seismic data processing is accepted by IEEE TGRS.
  • [2024-04] Gave a presentation @ Peking University.
  • [2023-10] One paper on 3D Object Recognition is accepted by IEEE TNNLS.
  • [2023-09] Gave a presentation @ Cambridge University.
  • [2023-07] Two first-authored papers on image fusion and depth map super-resolution are accepted by ICCV 2023 (1 Oral & 1 Poster).
  • [2023-05] Named one of the CVPR 2023 Outstanding Reviewers.
  • [2023-03] One first-authored paper on multi-modality image fusion is accepted by CVPR 2023.
  • [2022-06] Two papers on seismic data processing are accepted by IEEE TGRS.
  • [2022-03] One first-authored paper on guided depth map super-resolution is accepted by CVPR 2022 (Oral) .
  • [2021-04] One first-authored paper on model-driven image fusion is accepted by IEEE TCSVT.
  • [2021-03] Two papers (one first-authored) on remote sensing image processing are accepted by ICME 2021 (1 Oral & 1 Poster).
  • [2021-03] One paper on remote sensing image processing is accepted by CVPR 2021.
  • [2020-07] One first-authored paper on Bayesian image fusion is accepted by Signal Processing.
  • [2020-04] One first-authored paper on data-driven image fusion is accepted by IJCAI 2020.

PUBLICATIONS

You can find the full list on my Google Scholar.

Selected Publications

  1. Image Fusion via Vision-Language Model.
    • Zixiang Zhao, Lilun Deng, Haowen Bai, Yukun Cui, Zhipeng Zhang, Yulun Zhang, Haotong Qin, Dongdong Chen, Jiangshe Zhang, Peng Wang, Luc Van Gool
    • Accepted by ICML 2024
    • Introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information in different source images to guide image fusion
      [Project Page], [Paper], [ArXiv], [Code]
  2. Equivariant Multi-Modality Image Fusion.
    • Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc Van Gool
    • Accepted by CVPR 2024
    • Propose a novel end-to-end self-supervised fusion algorithm based on the equivariant sensing and imaging prior
      [Paper], [ArXiv], [Code]
  3. DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion.
    • Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu, Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc Van Gool
    • Accepted by ICCV 2023 (ORAL)
    • Propose a novel fusion algorithm based on the denoising diffusion sampling model
      [Paper], [ArXiv], [Code]
  4. Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution.
    • Zixiang Zhao, Jiangshe Zhang, Xiang Gu, Chengli Tan, Shuang Xu, Yulun Zhang, Radu Timofte, Luc Van Gool
    • Accepted by ICCV 2023
    • Propose a Spherical Space feature Decomposition network (SSDNet) and spherical contrast refinement for guided depth super-resolution
      [Paper], [ArXiv], [Code]
  5. CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion.
    • Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Shuang Xu, Zudi Lin, Radu Timofte, Luc Van Gool
    • Accepted by CVPR 2023
    • Propose a Correlation-Driven feature Decomposition Fusion (CDDFuse) network for multi-modality image fusion
      [Paper], [ArXiv], [Code]
  6. Discrete Cosine Transform Network for Guided Depth Map Super-Resolution.
    • Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Zudi Lin, Hanspeter Pfister
    • Accepted by CVPR 2022 (ORAL)
    • Propose an advanced Discrete Cosine Transform Network (DCTNet) for guided depth super-resolution
      [Paper], [ArXiv], [Code]
  7. Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion.
    • Zixiang Zhao, Jiangshe Zhang, Haowen Bai, Yicheng Wang, Yukun Cui, Lilun Deng, Kai Sun, Chunxia Zhang, Junmin Liu, Shuang Xu
    • Accepted by CVPR Workshop 2023
    • Gave three deep convolutional sparse coding networks for interpretable image fusion via unfolding the iterative shrinkage and thresholding algorithm
      [Paper], [ArXiv], [Code]
  8. Efficient and Model-Based Infrared and Visible Image Fusion via Algorithm Unrolling.
    • Zixiang Zhao, Shuang Xu, Jiangshe Zhang, Junmin Liu, Chunxia Zhang, Junmin Liu
    • Accepted by IEEE Transactions on Circuits and Systems for Video Technology (Top 1% Highly Cited Paper)
    • Presented an algorithm unrolling based interpretable deep image decomposition network for infrared and visible image fusion
      [Paper], [Arxiv], [Code]
  9. CACNN: Capsule Attention Convolutional Neural Networks for 3D Object Recognition.
    • Kai Sun, Jiangshe Zhang, Shuang Xu, Zixiang Zhao, Chunxia Zhang, Junmin Liu, Junying Hu
    • Accepted by IEEE Transactions on Neural Networks and Learning Systems
    • Presented an algorithm unrolling based interpretable deep image decomposition network for infrared and visible image fusion
      [Paper], [Arxiv], [Code]
  10. Hybrid Loss Guided Coarse-to-fine Model for Seismic Data Consecutively Missing Trace Reconstruction.
    • Xiaoli Wei, Chunxia Zhang, Hongtao Wang, Zixiang Zhao, Deng Xiong, Shuang Xu, Jiangshe Zhang, Sang-Woon Kim
    • Accepted by IEEE Transactions on Geoscience and Remote Sensing
    • Propose a coarse-to-fine seismic data consecutively missing traces interpolation
      [Paper], [Arxiv], [Code]
  11. Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network.
    • Hongtao Wang, Jiangshe Zhang, Zixiang Zhao, Chunxia Zhang, Long li, Zhiyu Yang, Weifeng Geng
    • Accepted by IEEE Transactions on Geoscience and Remote Sensing
    • Propose a multi-information fusion network to estimate stacking velocity from the fusion information of velocity spectra
      [Paper], [Arxiv], [Code]
  12. Deep Gradient Projection Networks for Pan-sharpening.
    • Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, Chunxia Zhang
    • Accepted by CVPR 2021 (Poster)
    • Develop a model-based deep pan-sharpening approach via Deep Gradient Projection Network
      [Paper], [ArXiv], [Code]
  13. FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter.
    • Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Lu Huang, Junmin Liu, Chunxia Zhang
    • Accepted by ICME 2021 (ORAL)
    • Propose a generative adversarial network for pansharpening via the fast guided filter and the spatial attention module
      [Paper], [ArXiv], [Code]
  14. DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion.
    • Zixiang Zhao*, Shuang Xu*, Chunxia Zhang, Junmin Liu, Jiangshe Zhang
    • Accepted by IJCAI 2020
    • Proposed a data-driven auto-encoder based network to accomplish the two-scale decomposition for image fusion
      [Paper], [ArXiv], [Code], [Accepted list]
  15. Bayesian Fusion for Infrared and Visible Images.
    • Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang
    • Accepted by Signal Processing
    • Established a Bayesian fusion model with a hierarchical Bayesian manner and the total-variation penalty, which can be inferred by the EM algorithm
      [Paper], [ArXiv], [Code]

Preprint

  1. Domain Adaptive Object Detection via Feature Separation and Alignment.
    • Chengyang Liang*, Zixiang Zhao*, Junmin Liu, Jiangshe Zhang
    • Establish a Feature Separation and Alignment Network (FSANet) for domain adaptive object detection
      [ArXiv], [Code]
  2. When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method.
    • Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Chunxia Zhang, Junmin Liu
    • Journal version of IJCAI2020 paper
      [ArXiv], [Code]

Professional Services

PC Member | Reviewer

Conferences

  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE International Conference on Computer Vision (ICCV)
  • European Conference on Computer Vision (ECCV)
  • Conference on Neural Information Processing Systems (NeurIPS)
  • International Conference on Learning Representations (ICLR)
  • International Joint Conference on Artificial Intelligence (IJCAI)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • Asian Conference on Computer Vision (ACCV)

Journals

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • International Journal of Computer Vision (IJCV)
  • IEEE Transactions on Image Processing (TIP)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • IEEE Transactions on Multimedia (TMM)
  • IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
  • IEEE Transactions on Computational Imaging (TCI)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • Information Fusion
  • Signal Processing
  • IEEE Signal Processing Letters (SPL)

Membership

  • IEEE Graduate Student Member
  • VALSE (Vision and Learning SEminar) Student Club Member
  • AI TIME PhD Branch Leader

Selected Talks

  • CVPR Shenzhen Pre-Conference Meeting.
    at: [04/2024] Peking University, Shenzhen, China

  • Prior Knowledge-Guided Multi-Modal Image Fusion.
    at: [02/2024] University of Electronic Science and Technology of China (UESTC), Online

  • Conference on Frontiers in Mathematics Doctoral Student: Prior Knowledge-Guided Multi-Modal Image Fusion.
    at: [10/2023] Peking University, Beijing, China

  • Multi-modal Image Fusion and Image Reconstruction [Website].
    at: [09/2023] Cambridge University, Online

  • VALSE (Vision and Learning SEminar) 2022: Low-level Vision Session Student Spotlight Talk [Website].
    at: [08/2022] Tianjin, China

  • AI TIME PhD Presentation (CVPR 2022): Discrete Cosine Transform Network for Guided Depth Map Super-Resolution [video (Chinese)].
    at: [08/2022] AI TIME & AMiner, Online

  • 2021 IJCAI-SAIA Young Elite Symposium: Deep Image Decomposition for Infrared and Visible Image Fusion [Website].
    at: [07/2021] World Artificial Intelligence Conference & Shanghai Jiao Tong University, Shanghai, China

  • AI TIME PhD Debate: Opportunities and Challenges in multimodal learning [video (Chinese)].
    at: [07/2021] AI TIME & AMiner, Beijing, China

  • AI TIME PhD Presentation (IJCAI 2020): Deep Image Decomposition for Infrared and Visible Image Fusion [video (Chinese)].
    at: [01/2021] AI TIME & AMiner, Beijing, China


More about me!

  • a die-hard fan of Kobe Bryant and Los Angeles Lakers
  • have a passionate love for basketball
  • love swimming, skiing, singing…
  • a little guitar and violin