Tel:
Email: dengwj16@sz.tsinghua.edu.cn
Address:
2019/07 – 2023/10 Australian National University,Computer Science,PhD
2019/07 – 2023/10 University of Chinese Academy of Sciences,Computer Application Technology,Master
2012/07 – 2016/09 Beijing Jiaotong University, Electronic Science and Technology,Bachelor
2026/06 -now,Assistant Professor, Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University
2025/02 - 2026/05, Australian National University, Researcher
2023/01 - 2025/01, Australian National University, Research Fellow
Transactions on Machine Learning Research Action Editor
Self-Evaluating, Self-Exploring, and Self-Improving AI Agents
Developing AI agents that can autonomously evaluate their capabilities, explore unknown environments, and continuously improve themselves through feedback-driven learning.
Self-Evaluating, Self-Exploring, and Self-Improving AI Agents
Developing AI agents that can autonomously evaluate their capabilities, explore unknown environments, and continuously improve themselves through feedback-driven learning.
1. Weijian Deng, Dylan Campbell, Chunyi Sun, Shubham Kanitkar, Matthew E. Shaffer, and Stephen Gould. Differentiable neural surface refinement for modeling transparent objects[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2024: 20268–20277.
2. Weijian Deng*, Yumin Suh, Stephen Gould, and Liang Zheng. Confidence and dispersity speak: Confidence and dispersity speak: Characterizing prediction matrix for unsupervised accuracy estimation[C]//Proceedings of the International Conference on Machine Learning (ICML). PMLR, 2023: 7658–7674.
3. WeijianDeng, Stephen Gould, and Liang Zheng.On the strong correlation between model invariance and generalization[C]//Advances in Neural Information Processing Systems (NeurIPS). 2022: 28052–28067.
4. WeijianDeng, and Liang Zheng*. AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec. 2021, 46(3): 1868–1880.
5. Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao*. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018: 994–1003.
6. Weijian Deng, Stephen Gould, Liang Zheng*. What does rotation prediction tell us about classifier accuracy under varying testing environments? [C]//Proceedings of the International Conference on Machine Learning (ICML), 2021: 2579–2589.
7. Weijian Deng, Dylan Campbell, Chunyi Sun, Jiahao Zhang, Shubham Kanitkar, Matthew E. Shaffer, Stephen Gould. Pos3r: 6D pose estimation for unseen objects made easy[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025: 16818–16828.
8. Yuli Zou#*, Weijian Deng#, Liang Zheng. Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023: 19333–19342.
9. Weijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng*. Fine-grained classification via categorical memory networks[J]. IEEE Transactions on Image Processing, 2022, 31: 4186–4196.
10. WeijieTu, Weijian Deng*, and Tom Gedeon. Toward a Holistic Evaluation of Robustness in CLIP Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 2025, 47(9): 8280–8296.
1. Weijian Deng, Dylan Campbell, Chunyi Sun, Shubham Kanitkar, Matthew E. Shaffer, and Stephen Gould. Differentiable neural surface refinement for modeling transparent objects[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2024: 20268–20277.
2. Weijian Deng*, Yumin Suh, Stephen Gould, and Liang Zheng. Confidence and dispersity speak: Confidence and dispersity speak: Characterizing prediction matrix for unsupervised accuracy estimation[C]//Proceedings of the International Conference on Machine Learning (ICML). PMLR, 2023: 7658–7674.
3. WeijianDeng, Stephen Gould, and Liang Zheng.On the strong correlation between model invariance and generalization[C]//Advances in Neural Information Processing Systems (NeurIPS). 2022: 28052–28067.
4. WeijianDeng, and Liang Zheng*. AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec. 2021, 46(3): 1868–1880.
5. Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao*. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018: 994–1003.
6. Weijian Deng, Stephen Gould, Liang Zheng*. What does rotation prediction tell us about classifier accuracy under varying testing environments? [C]//Proceedings of the International Conference on Machine Learning (ICML), 2021: 2579–2589.
7. Weijian Deng, Dylan Campbell, Chunyi Sun, Jiahao Zhang, Shubham Kanitkar, Matthew E. Shaffer, Stephen Gould. Pos3r: 6D pose estimation for unseen objects made easy[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025: 16818–16828.
8. Yuli Zou#*, Weijian Deng#, Liang Zheng. Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023: 19333–19342.
9. Weijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng*. Fine-grained classification via categorical memory networks[J]. IEEE Transactions on Image Processing, 2022, 31: 4186–4196.
10. WeijieTu, Weijian Deng*, and Tom Gedeon. Toward a Holistic Evaluation of Robustness in CLIP Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 2025, 47(9): 8280–8296.
国家青年人才项目，2025
DAAD AINeT Fellow in Explainable AI, 2025
CVPR 2025 Outstanding Reviewer, 2025
NeurIPS 2024 Top Reviewer, 2024
ACM MM 2024 Outstanding Area Chair, 2024
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
