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. Looking forward to cooperation in this direction.
Email  / 
CV  / 
Google Scholar
 / 
Github
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News
[Apr 2023] One paper on model (out-of-distribution) generalization prediction is accepted to ICML 2023
[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]
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Research
My research papers focus on model generalization, domain
adaptation, and object recognition.
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Confidence and Dispersity Speak: Characterising Prediction Matrix for
Unsupervised Accuracy Estimation
Weijian Deng, Yumin Suh, Stephen Gould, and Liang Zheng
ICML 2023 [Paper]
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On the Strong Correlation Between Model Invariance and
Generalization
Weijian Deng, Stephen Gould, and Liang Zheng
NeurIPS 2022 (Spotlight) [Paper,
OpenReview,
Slides,
Poster,
BibTex]
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AutoEval: Are Labels Always Necessary for Classifier Accuracy
Evaluation?
Weijian Deng and Liang Zheng
IEEE TPAMI 2022 [Project,
BibTex,
Paper]
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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]
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Are Labels Always Necessary for Classifier Accuracy Evaluation?
Weijian Deng and Liang Zheng
CVPR 2021 [Paper,
Project,
BibTex,
Poster,
Slides]
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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]
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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")
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Fine-grained Classification via Categorical Memory Networks
Weijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng
IEEE TIP 2022 [
BibTex,
Paper
]
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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
]
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A Bag-of-Prototypes Representation for Dataset-Level Applications
Weijie Tu, Weijian Deng, Tom Gedeon, and Liang Zheng
CVPR 2023
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SVDNet for Pedestrian Retrieval
Yifan Sun, Liang Zheng, Weijian Deng, Shengjin Wang
ICCV 2017 (Spotlight) [Paper,
Code,
BibTex,
Poster]
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Ranking Models in Unlabeled New Environments
Xiaoxiao Sun,Yunzhong Hou,Weijian Deng, Hongdong Li, Liang Zheng
ICCV 2021 [Paper,
Code]
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Academic Activity
Reviewer: NeurIPS 2022-2023; ICML 2022-2023; ICLR 2022-2023; ICCV 2021, 2023; CVPR
2021-2023; ECCV 2020; ACM MM 2020-2023; 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)
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This website is modified based on source
code. Thank you, Jonathan!
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