Wenliang Zhao

I am a fourth-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 model architectures and generative models.

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Publications

* indicates equal contribution

UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.

VPD (Visual Perception with Pre-trained Diffusion Models) is a framework that leverages the high-level and low-level knowledge of a pre-trained text-to-image diffusion model to downstream visual perception tasks.

HorNet is a family of generic vision backbones that perform explicit high-order spatial interactions based on Recursive Gated Convolution.

DenseCLIP is a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.

Global Filter Networks is a transformer-style architecture that learns long-term spatial dependencies in the frequency domain with log-linear complexity.

We present a dynamic token sparsification framework to prune redundant tokens in vision transformers progressively and dynamically based on the input.

We present a framework (DIML) to add interpretability to metric learning and improve the performance of deep metric learning models.

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.

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