Weijian Deng @ANU

Weijian Deng

I am a Research Fellow at the Australian National University, working with Prof Stephen Gould. My current research focuses are Neural Radiance Fields and Out-of-Distribution Generalization.
Previously, I was a PhD student at the Australian National University, where I worked on model generalization prediction, working with Prof Stephen Gould, Dr Yumin Suh, and Dr Liang Zheng.

PhD Research Topic: Model (Out-of-Distribution) Generalization Prediction

My research aspiration is to build robust and reliable machine perception models that exhibit exceptional generalization capabilities across various distribution shifts. Machine perception algorithms are commonly devised and assessed based on oversimplified assumptions that frequently fail to hold true in real-world scenarios. My research is focused on developing innovative unsupervised generalization prediction.
I am interested in exploring an important but largely unexplored question: Are Labels Always Necessary for Model Evaluation? The primary focus lies in the accurate prediction of model generalization across diverse testing environments without relying on human annotations. The significance of this investigation lies in its potential to identify and diagnose potential failure cases, while also providing valuable guidance for future model training endeavors. Here are [some slides] to introduce my research.

Email / CV / Google Scholar / Github / Linkedin

News

[Oct 2023] One paper on novel view synthesis of refrative objects is accepted to WACV 2024

[Sep 2023] One paper on robustness of visual foundation model is accepted to NeurIPS 2023

[Jul 2023] One paper on (out-of-distribution) predictive calibration is accepted to ICCV 2023

[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 on generalization prediction is accepted to ICML 2021 [Paper, Project]

[Mar 2021] One paper on generalization prediction 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 works focus on out-of-distribution robustness, model generalization and object recognition.

	Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation Weijian Deng, Yumin Suh, Stephen Gould, Liang Zheng ICML 2023 [Paper, Slides, Poster, BibTex, Codes ]
	On the Strong Correlation Between Model Invariance and Generalization Weijian Deng, Stephen Gould, Liang Zheng NeurIPS 2022 (Spotlight) [Paper, OpenReview, Slides, Poster, BibTex]
	AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation? Weijian Deng, 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, Liang Zheng ICML 2021 [Paper, BibTex, Project, Slides, Poster]
	Are Labels Always Necessary for Classifier Accuracy Evaluation? Weijian Deng, Liang Zheng CVPR 2021 [Paper, Project, BibTex, Poster, Slides]
	Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios Yuli Zou, Weijian Deng (equal contribution), Liang Zheng ICCV 2023 [Paper, Poster, Code, BibTex ]
	A Bag-of-Prototypes Representation for Dataset-Level Applications Weijie Tu, Weijian Deng, Tom Gedeon, Liang Zheng CVPR 2023 [Paper, Code, BibTex, Poster]
	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]
	Similarity-preserving Image-image Domain Adaptation for Person Re-identification Weijian Deng, Liang Zheng, Qixiang Ye, Yi Yang, Jianbin Jiao Arxiv 2019 [Paper]
	Domain Alignment with Triplets Weijian Deng, Liang Zheng, Jianbin Jiao Arxiv 2019 [Paper]
	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 ]
	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, BibTex ]

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)

Website design from Jon Barron.

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

On the Strong Correlation Between Model Invariance and Generalization

AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?

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

Are Labels Always Necessary for Classifier Accuracy Evaluation?

Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios

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

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

Similarity-preserving Image-image Domain Adaptation for Person Re-identification

Domain Alignment with Triplets

Rethinking Triplet Loss for Domain Adaptation

Fine-grained Classification via Categorical Memory Networks

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

SVDNet for Pedestrian Retrieval

Ranking Models in Unlabeled New Environments