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I am a Research Fellow at the Australian National University, collaborating with Prof. Stephen Gould. My research primarily focuses on 3D Content Modeling and Machine Learning Safety. I am also fortunate to work alongside Dr. Dylan Campbell. Previously, I pursued my PhD at the same institution, where I explored model generalization prediction. My work involved close collaboration with Prof. Stephen Gould, Dr. Yumin Suh, and Dr. Liang Zheng. Robust Foundational Vision-Language Models: My research centers on improving the robustness and reliability of AI systems, with a particular emphasis on vision-language models. I am currently exploring model generalization prediction, developing methods to estimate model accuracy on unlabeled test sets. Additionally, I investigate model robustness, focusing on the geometric understanding within CLIP models and assessing their sensitivity to variations in visual factors. Developing Advanced Simulation and 3D Modeling Techniques: I am also interested in creating advanced simulation frameworks using neural rendering and large language models (LLMs). These simulations are designed to enhance AI adaptability and reliability, especially in autonomous systems, by accurately reflecting complex, real-world scenarios and incorpo- rating physics-based principles into 3D modeling. Email / CV / Google Scholar / Github / Linkedin / PhD Thesis |
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My research works focus on Predicting Model Generalization, Monitoring Model Reliability, Enhancing Visual Recognition, and 3D Modeling & Generation. |
I. Predicting Model Generalization
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MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution ShiftsRenchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng, Jianfeng Zhang, Bo An |
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What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?Weijie Tu, Weijian Deng, Liang Zheng, Tom Gedeon |
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A Bag-of-Prototypes Representation for Dataset-Level ApplicationsWeijie Tu, Weijian Deng, Tom Gedeon, Liang Zheng |
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Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy EstimationWeijian Deng, Yumin Suh, Stephen Gould, Liang Zheng |
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AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?Weijian Deng, Liang Zheng |
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What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?Weijian Deng, Stephen Gould, Liang Zheng ICML 2021 (Spotlight) [Paper, BibTex, Project, Slides, Poster] |
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Are Labels Always Necessary for Classifier Accuracy Evaluation?Weijian Deng, Liang Zheng |
II. Monitoring Model Reliability
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An Empirical Study into What Matters for Calibrating Vision-Language ModelsWeijie Tu, Weijian Deng, Dylan Campbell, Stephen Gould, Tom Gedeon |
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A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)Weijie Tu, Weijian Deng, Tom Gedeon NeurIPS 2023 [Paper, BibTex, OpenReview ] |
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Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution ScenariosYuli Zou*, Weijian Deng* (equal contribution), Liang Zheng |
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On the Strong Correlation Between Model Invariance and GeneralizationWeijian Deng, Stephen Gould, Liang Zheng NeurIPS 2022 (Spotlight) [Paper, OpenReview, Slides, Poster, BibTex] |
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Ranking Models in Unlabeled New EnvironmentsXiaoxiao Sun, Yunzhong Hou, Weijian Deng, Hongdong Li, Liang Zheng |
III. 3D Modeling & Generation
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Ray Deformation Networks for Novel View Synthesis of Refractive ObjectsWeijian Deng, Dylan Campbell, Chunyi Sun, Shubham Kanitkar, Matthew Shaffer, Stephen Gould. |
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Differentiable Neural Surface Refinement for Transparent ObjectsWeijian Deng, Dylan Campbell, Chunyi Sun, Shubham Kanitkar, Matthew Shaffer, Stephen Gould. |
IV. Enhancing Visual Recognition
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Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identificationWeijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao |
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Similarity-preserving Image-image Domain Adaptation for Person Re-identificationWeijian Deng, Liang Zheng, Qixiang Ye, Yi Yang, Jianbin Jiao Arxiv 2019 [Paper] |
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Domain Alignment with TripletsWeijian Deng, Liang Zheng, Jianbin Jiao Arxiv 2019 [Paper] |
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Rethinking Triplet Loss for Domain AdaptationWeijian 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 NetworksWeijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng |
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Split to Learn: Gradient Split for Multi-Task Human Image AnalysisWeijian Deng, Yumin Suh, Xiang Yu, Masoud Faraki, Liang Zheng, Manmohan Chandraker |
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SVDNet for Pedestrian RetrievalYifan Sun, Liang Zheng, Weijian Deng, Shengjin Wang |
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Action Editor, Transactions on Machine Learning Research Lecturer, "Introduction to Computer Science", SDUW (Joint ANU-SDUW Program, Winter Semester 2023) ACM Multimedia 2024 Area Chair Reviewer: NeurIPS 2022-2023; ICML 2022-2024; ICLR 2022-2023; ICCV 2021, 2023; CVPR 2021-2024; ECCV 2020; ACM MM 2020-2023; IEEE-TPAMI; IEEE-TIP; IJCV 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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NeurIPS 2023 Top Reviewer, 2023 ICML 2022 Top 10% Reviewer, 2022 ECCV 2020 Outstanding Reviewer, 2022 Australian Government Research Training Program (AGRTP) Scholarship, 2019-2023 The Third Place in Vehicle Re-identification track of CVPR 2019 AI-City Challenge, 2019 China National Scholarship (Master), 2018 China National Scholarship (Bachelor), 2014, 2015 |
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Academic research grant from the Google PaliGemma Academic Program, 2024 - 2025 Academic research grant from the Google Cloud Research Credits Program, 2024 - 2025 ICML Early-Career Travel Fund, 2024 ANU Early-Career Travel Fund, 2024 ANU-SDUW Teaching Fellowship, 2023 NeurIPS 2022 Scholar Award, 2022 |
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Website design from Jon Barron.
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