I am a Lecturer at ETH Zürich. I work with Prof. Luca Benini and Prof. Luc Van Gool. I also collaborate closely with Dr. Radu Timofte and Dr. Michele Magno.
My research focus on efficient deep learning and artificial intelligence algorithms and systems with applications to vision, language, and biosignals. In particular, I am interested in the following topics:
We have open positions and projects on the topics. Please check the details about the projects in the following section.
We are looking for self-motivated PhD and Master students working on the development, optimization, and deployment of foundation models. In particular, we have the following projects and open positions.
| Apr. 2024 | Report for the 9th NTIRE Challenge on Efficient SR is online now. |
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| Mar. 2024 | We open PhD positions on Secure Machine Learning on RISC-V Servers and Accelerators. at ETH Zürich |
| Feb. 2024 | One paper is accepted by CVPR 2024! Check the paper here. |
| Feb. 2024 | VRT is accepted by IEEE TIP! Check the paper here. |
| Feb. 2024 | KGT paper is online. Check the paper here. |
| Jan. 2024 | We are organizing the 9th NTIRE Challenge on Efficient Super-Resolution. |
| Dec. 2023 | I start as a Lecturer at ETH Zürich! |
| Nov. 2023 | Our work on smart glasses is online. Check the paper here. |
| Sep. 2023 | One paper is accepted by Machine Intelligence Research. Check the paper here. |
| Aug. 2023 | TinyTracker is accepted by IEEE Sensors. Check the paper here. |
| Apr. 2023 | LSDIR dataset is accepted as a workshop paper at CVPR 2023. Check the paper here. |
| Mar. 2023 | One paper is accepted by ICME. Check the paper here. |
| Feb. 2023 | Two papers are accepted by CVPR2023. GRL sets the new state-of-the-art for 7 image restoration tasks. |
| Jan. 2023 | We are organizing the NTIRE 2023 Challenge on Efficient Super-Resolution, Image Denosing, and Image Super-Resolution (x4). |
| Jul. 2022 | One paper is accepted by ECCV 2022. |
| Apr. 2022 | I start as a Postdoc researcher at the Computer Vision Lab of ETH Zürich! |
| Feb. 2022 | I defended my thesis! |
| Feb. 2022 | Random pruning paper is accepted by CVPR 2022. | Jan. 2022 | We are organizing the NTIRE 2022 Challenge on Efficient Super-Resolution. |
| Jul. 2021 | Efficient GCN paper is accepted by ICCV 2021! |
| Apr. 2021 | Our work on efficient graph convolutional networks is available on arXiv! Check the paper here. |
| Mar. 2021 | Three papers are accepted by CVPR 2021! Check the papers here. The Heterogeneity Hypothesis. DASR. MOCDA. |
| Dec. 2020 | I'm going to serve as as a Senior PC member for IJCAI 2021. |
| Sep. 2020 | DPIR paper is online. Check the paper here. |
| Aug. 2020 | Code of DHP is available. Check it here. |
| Jul. 2020 | DHP paper is accepted by ECCV2020! |
| Jul. 2020 | The Heterogeneity Hypothesis paper is online now. Check it on Arxiv. |
| Apr. 2020 | DASR paper is online (Arxiv). |
| Mar. 2020 | DHP paper is online (Arxiv) |
| Feb. 2020 | One paper accepted by CVPR2020. |
| Jul. 2019 | One paper accepted by ICCV2019. |
| Jul. 2019 | I won the best poster presentation at ICVSS. |
| Feb. 2019 | One paper accepted by CVPR2019. |
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Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary |
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Efficient and explicit modelling of image hierarchies for image restoration |
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LocalViT: Analyzing Locality in Vision Transformers |
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Revisiting Random Channel Pruning for Neural Network Compression |
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Towards Efficient Graph Convolutional Networks for Point Cloud Handling |
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Plug-and-Play Image Restoration with Deep Denoiser Prior |
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The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture |
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Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training |
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Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation |
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DHP: Differentiable Meta Pruning via HyperNetworks |
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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results |
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Group sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression |
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Learning filter basis for convolutional neural network compression |
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Self-guided network for fast image denoising |
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3D appearance super-resolution with deep learning |
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CARN: Convolutional anchored regression network for fast and accurate single image super-resolution |
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Multiview Video Super-Resolution via Information Extraction and Merging |
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