I am currently a Ph.D student at Computer Vision Lab supervised by Prof. Luc Van Gool. I am also co-supervised by Dr. Radu Timofte. My research interests include efficient neural network design, neural network compression and acceleration, image restoration, and graph neural networks. Specifically, I developed filter basis learning and pruning methods for network compression and acceleration. During my study, I am fortunate to work with a couple of brilliant senior researchers including Dr. Shuhang Gu, Dr. Kai Zhang, Dr. Martin Danelljan, Dr. Wen Li from the Computer Vision Lab and Dr. Vagia Tsiminaki, Dr. Zhaopeng Cui from the Computer Vision and Geometry Group led by Prof. Marc Pollefeys.
LocalViT: Bringing Locality to Vision Transformers |
Towards Efficient Graph Convolutional Networks for Point Cloud Handling |
Plug-and-Play Image Restoration with Deep Denoiser Prior |
The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture |
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training |
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation |
DHP: Differentiable Meta Pruning via HyperNetworks |
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results |
Group sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression |
Learning filter basis for convolutional neural network compression |
Self-guided network for fast image denoising |
3D appearance super-resolution with deep learning |
CARN: Convolutional anchored regression network for fast and accurate single image super-resolution |
Multiview Video Super-Resolution via Information Extraction and Merging |
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