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

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]

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 ICML 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]
	Ranking Models in Unlabeled New Environments Xiaoxiao Sun,Yunzhong Hou,Weijian Deng, Hongdong Li, Liang Zheng ICCV 2021 [Paper, Code]