Wenliang Zhao

I am a third year Ph.D student in the Department of Automation at Tsinghua University, advised by Prof. Jiwen Lu and Prof. Jie Zhou. In 2020, I obtained my B.Eng. in the Department of Automation, Tsinghua University.

I am broadly interested in computer vision and deep learning. My current research focuses on interpretable machine learning and action recognition.

Email / Google Scholar / Github

News

2022-03: Check out our work at CVPR 2022 on language-guided dense prediction (DenseCLIP).

2021-09: GFNet and DynamicViT are accepted to NeurIPS 2021.

2021-07: 2 papers on video understanding and interpretable metric learning are accepted to ICCV 2021.

Publications

* indicates equal contribution

	HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions Yongming Rao, Wenliang Zhao, Yansong Tang, Jie Zhou , Ser-Nam Lim , Jiwen Lu NeurIPS, 2022 [arXiv] [Code] [Project Page] [中文解读] HorNet is a family of generic vision backbones that perform explicit high-order spatial interactions based on Recursive Gated Convolution.
	DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Jie Zhou, Jiwen Lu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 [arXiv] [Code] [Project Page] [中文解读] DenseCLIP is a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.
	Global Filter Networks for Image Classification Yongming Rao, Wenliang Zhao, Zheng Zhu , Jiwen Lu , Jie Zhou Conference on Neural Information Processing Systems (NeurIPS), 2021 [arXiv] [Code] [Project Page] [中文解读(By HappyAIWalker)] Global Filter Networks is a transformer-style architecture that learns long-term spatial dependencies in the frequency domain with log-linear complexity.
	DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification Yongming Rao, Wenliang Zhao, Benlin Liu , Jiwen Lu , Jie Zhou , Cho-Jui Hsieh Conference on Neural Information Processing Systems (NeurIPS), 2021 [arXiv] [Code] [Project Page] [知乎] We present a dynamic token sparsification framework to prune redundant tokens in vision transformers progressively and dynamically based on the input.
	Towards Interpretable Deep Metric Learning with Structural Matching Wenliang Zhao, Yongming Rao, Ziyi Wang, Jiwen Lu , Jie Zhou IEEE International Conference on Computer Vision (ICCV), 2021 [arXiv] [Code] We present a framework (DIML) to add interpretability to metric learning and improve the performance of deep metric learning models.
	Group-aware Contrastive Regression for Action Quality Assessment Xumin Yu, Yongming Rao, Wenliang Zhao, Jiwen Lu , Jie Zhou IEEE International Conference on Computer Vision (ICCV), 2021 We propose a new contrastive regression (CoRe) framework to learn the relative scores by pair-wise comparison, which highlights the differences between videos and guides the models to learn the key hints for assessment.

Honors and Awards

2020 Outstanding Undergraduate, Tsinghua University

2018 Tang Lixin Scholarship, Tsinghua University

2019 Tsinghua Presidential Award Nomination, Tsinghua University

2018 Zheng Weimin Scholarship, Tsinghua University

2018 Jiang Nanxiang Scholarship, Tsinghua University

2018 1st prize in 36th Challenge Cup, Tsinghua University

2017 Qualcomm Scholarship

2017 National Scholarship, Tsinghua University

Academic Services

Conference Reviewer CVPR 2022, ECCV 2022, ICME 2022

Journal Reviewer T-IP

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