Wanhua Li

I am a fifth year Ph.D student in the Department of Automation at Tsinghua University, advised by Prof. Jiwen Lu and Prof. Jianjiang Feng .

In 2017, I received my B.S. degree in computer science at Sun Yat-sen University, Guangzhou, China.

My research interests lie in computer vision and deep learning, particularly facial attribute analysis, graph neural networks, and meta learning.

Note: I expect to graduate in June 2022 and am pursuing a postdoctoral position. If you're interested in me, please let me know and do not hesitate to contact me.

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We propose a Mask-Robust Inpainting Network (MRIN) to recover the masked areas of an image with a patch-wise inpainting process.

We develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity for graph-based kinship verification.

We propose a Frequency-Aware Spatiotemporal Transformer for video inpainting detection, which simultaneously mines the traces of video inpainting from spatial, temporal, and frequency domains.

We propose probabilistic ordinal embeddings to empower the present-day regression methods with the ability of uncertainty estimation.

A Discriminative Sample Meta-Mining strategy is proposed to mine discriminative information from limited positive pairs and sufficient negative samples for kinship verification.

It is the first face clustering method to train on very large-scale graph with 20M nodes, and achieve superior inference results on 12M testing data.

A simpler, faster, and more accurate method for social relation recognition.

We considers how to compare and fuse the extracted feature pair to reason about the kin relations with the proposed graph-based kinship reasoning networks.

We propose BridgeNet for age estimation, which aims to mine the continuous relation between age labels effectively.

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