Ziwei Zhao (赵子为)

Bio

Ziwei Zhao

I am a Ph.D student in the computer vision lab at Indiana University, led by Prof. David Crandall. My research focus is egocentric vision and explainable AI.

I am currently looking for internship opportunities, please contact me if interested.

Google Scholar

My CV

Education

Ph.D. in Computer Science
Indiana University Bloomington, Ph.D advisor: Prof. David Crandall (~Present)

M.S. in Electrical Engineering
Washington University in St. Louis (2017)

B.S. in Astrophysics
University of Science and Technology of China (2015)

Research Projects

Egocentric Vision

Designed and tested a new framework for identifying first-person camera wearers in third-person views. CVPR2024

Leading data collection for Ego-exo4D dataset at Indiana University, baseline author for the Ego-exo4D translation challenge. CVPR2024 Oral

Participated in data collection for Ego4D dataset at Indiana University. CVPR2022 Oral

Explainable AI in Deep Learning and Case-based Reasoning

Designed multiple prototype models for combining deep learning and case-based reasoning to enhance system accuracy and explainability. Publications are listed below.

All Publications

  1. Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views
    Ziwei Zhao, Yuchen Wang, Chuhua Wang, David Crandall
    CVPR2024 [Code]

  2. Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
    Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, … , Ziwei Zhao, … , Michael Wray
    CVPR2024 Oral [Project Website]

  3. Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric
    Ziwei Zhao, David Leake, Xiaomeng Ye, David Crandall
    IJCAI2024 Explainable AI workshop

  4. Learning Analogies between Classes to Create Counterfactual Explanations
    Xiaomeng Ye, David Leake, Yu Wang, Ziwei Zhao, David Crandall
    IJCAI2024 Workshop on the Interactions between Analogical Reasoning and Machine Learning

  5. Towards Network Implementation of CBR: Case Study of a Neural Network K-NN Algorithm
    Xiaomeng Ye, David Leake, Yu Wang, Ziwei Zhao, David Crandall
    ICCBR2024 (International Conference on Case-Based Reasoning)

  6. Selecting Feature Changes for Counterfactual Explanation: A Class-to-Class Approach
    Xiaomeng Ye, David Leake, Yu Wang, Ziwei Zhao, David Crandall
    IJCAI2023 Explainable AI workshop

  7. Ego4d: Around the world in 3,000 hours of egocentric video
    Kristen Grauman, Andrew Westbury, Eugene Byrne, …, Ziwei Zhao, …, Jitendra Malik
    CVPR2022 Oral [Project Website]

  8. Generating Counterfactual Images: Towards a C2C-VAE Approach
    Ziwei Zhao, David Leake, Xiaomeng Ye, David Crandall
    ICCBR2022 Explainable CBR workshop

  9. Generation and Evaluation of Creative Images from Limited Data: A Class-to-Class VAE Approach
    Xiaomeng Ye, Ziwei Zhao, David Leake, David Crandall
    ICCC2022 (International Conference on Computational Creativity)

  10. Applying the case difference heuristic to learn adaptations from deep network features
    Xiaomeng Ye, Ziwei Zhao, David Leake, Xizi Wang, David Crandall
    IJCAI2021 DeepCBR workshop

Work Experience

Intern @ Horizon Robotics (2018)

Algorithm Engineer @ Beijing IDriverPlus (2017-2018)

Services

Conference Reviewer: CVPR2023, CVPR2024, ECCV2024

Other Reviewer: T4V workshop@CVPR2024

Photography

I love traveling and doing photography, here is my work

Some of my favorite photos:

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Format based on https://github.com/ShawhinT/example-portfolio