Weijian Deng

I am a Research Fellow at Australian National University, working with Prof Stephen Gould. Previously, I was a PhD student at the Australian National University, where I worked on model generalization and was supervised by Dr Liang Zheng, Dr Yumin Suh, and Prof Stephen Gould.

Current Research Focus: Neural Radiance Fields.

PhD Research Topic: Model (Out-of-Distribution) Generalization. Over the past three years, I have been exploring an answer to one question: Are Labels Always Necessary for Model Evaluation? Specifically, the goal is to estimate the model generalization on various unlabeled test datasets. Please check [ TPAMI 2022/CVPR 2021 work] and [ ICML 2021 work]. Here are [some slides] to introduce my research.

Email  /  CV  /  Google Scholar  /  Github

profile photo
News

  • [Feb 2023] One paper on dataset-level analysis is accepted to CVPR 2023
  • [Jan 2023] Started the Research Fellow position
  • [Jan 2023] Submitted PhD thesis
  • [Dec 2022] Completed PhD oral presentation
  • [Oct 2022] One paper on multi-task learning is accepted to WACV 2023
  • [Oct 2022] Honoured to receive NeurIPS 2022 Scholar Award (Travel Award)
  • [Sep 2022] One paper on model invariance and generalization is accepted to NeurIPS 2022 [Paper]
  • [Jul 2022] Honoured to be recognized by ICML 2022 as a top 10% reviewer
  • [Jun 2022] We will organize the CVPR 2022 Tutorial on ["Evaluating Models Beyond the Textbook: Out-of-distribution and Without Labels"]
  • [May 2022] One paper on fine-grained clasification is accepted to IEEE TIP [Paper]
  • [Dec 2021] One paper on model decision understanding is accepted to IEEE TPAMI [Project]
  • [May 2021] One paper is accepted to ICML 2021 [Paper, Project]
  • [Mar 2021] One paper is accepted to CVPR 2021 [Paper, Project]
  • [Jul 2020 - Sep 2020] Summer intern in NEC Labs America. Wonderful experience with my talented mentors Yumin Suh, Xiang Yu, Masoud Faraki, and Manmohan Chandraker.
  • [Aug 2020] VisDA-2020 challenge successfully ends. Congratulations to the [final teams]!
  • Big thanks to all committee members Kate Saenko, Liang Zheng, and Xingchao Peng!
  • [Jun 2020] Honoured to be recognized by ECCV2020 as a top reviewer
  • [Jan 2020] One paper is accepted to IEEE TCSVT [Paper]
  • [Jul 2020] Honoured to be recognized by ECCV2020 as a top reviewer [Link]
  • [Jul 2019] Study PhD program at The Australian National University. My reseach is supported by Australian Governmen Scholarship [AGRTP].
  • [Jun 2019] Receive M.Eng. from the University of the Chinese Academy of Sciences
  • [Jun 2019] Win 3rd place out of 84 participants in vehicle re-identification in CVPR 2019 AI-City Challenge
  • [Paper, Code]
  • [Jul 2018 - Nov 2018] Reserch assistant in Singapore University of Technology and Design (SUTD)
  • [Mar 2018] One paper is accepted to CVPR 2018 [Paper, Code]
  • [Jul 2017] One paper is accepted to ICCV 2017 [Paper, Code]
  • Research

    My research papers focus on model generalization, domain adaptation, and object recognition.

    Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation

    Weijian Deng, Yumin Suh, Stephen Gould, and Liang Zheng

    Arxiv 2023 [Paper]

    On the Strong Correlation Between Model Invariance and Generalization

    Weijian Deng, Stephen Gould, and Liang Zheng

    NeurIPS 2022 (Spotlight) [Paper, OpenReview, Slides, Poster, BibTex]

    AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?

    Weijian Deng and Liang Zheng

    IEEE TPAMI 2022 [Project, BibTex, Paper]

    What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

    Weijian Deng, Stephen Gould, and Liang Zheng

    ICML 2021 [Paper, BibTex, Project, Slides, Poster]

    Are Labels Always Necessary for Classifier Accuracy Evaluation?

    Weijian Deng and Liang Zheng

    CVPR 2021 [Paper, Project, BibTex, Poster, Slides]

    Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

    Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao

    CVPR 2018 [Paper, Code, BibTex, Poster]

    Rethinking Triplet Loss for Domain Adaptation

    Weijian Deng, Liang Zheng, Yifan Sun, Jianbin Jiao

    IEEE TCSVT 2020 [Paper, BibTex]

    (Journal version of "Domain alignment with triplets")

    Fine-grained Classification via Categorical Memory Networks

    Weijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng

    IEEE TIP 2022 [ BibTex, Paper ]

    Split to Learn: Gradient Split for Multi-Task Human Image Analysis

    Weijian Deng, Yumin Suh, Xiang Yu, Masoud Faraki, Liang Zheng, Manmohan Chandraker

    WACV 2023 [ Paper, US Patent, Poster, Slides, BibTex ]

    A Bag-of-Prototypes Representation for Dataset-Level Applications

    Weijie Tu, Weijian Deng, Tom Gedeon,and Liang Zheng

    CVPR 2023

    SVDNet for Pedestrian Retrieval

    Yifan Sun, Liang Zheng, Weijian Deng, Shengjin Wang

    ICCV 2017 (Spotlight) [Paper, Code, BibTex, Poster]

    Academic Activity

    Reviewer: NeurIPS 2022; ICML 2022-2023; ICLR 2022-2023; ICCV 2021, 2023; CVPR 2021-2023; ECCV 2020; ACM MM 2020-2022; IEEE-TPAMI; IEEE-TIP

    Co-organizer: ECCV 2020 Workshop on "Visual Domain Adaptation Challenge"

    Co-organizer: CVPR 2022 Tutorial on "Evaluating Models Beyond the Textbook: Out-of-distribution and Without Labels"

    Guest speaker: SUTD 2018/12 (image-image translation); ANU 2019/09 (SVDNet)


    This website is modified based on source code. Thank you, Jonathan!