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I am a PhD student (2019.09-) at the State Key Laboratory of Software Development Environment
and Shen Yuan Honors College, Beihang University,
supervised by Prof. Wei Li
and Prof. Xianglong Liu.
And I am a visiting PhD student (2022.10-) at the Computer Vision Laboratory, ETH Zürich, supervised by Prof. Fisher Yu.
I obtained my BSc degree in Computer Science and Engineering (Summa Cum Laude) from Beihang University (2015.09-2019.07).
I interned at ByteDance AI Lab, Tencent WeiXin Group, and Microsoft Research Asia in 2019-2022.
Google Scholar / Github / Linkedin / CV |
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I'm interested in network binarization and quantization. And my research goal is to enable state-of-the-art neural network models to be deployed on resource-limited hardware, including the compression for different neural architectures, and the flexible deployment on various hardware. |
[2023.02] One first-authored paper for FSMN binarization is accepted by IEEE TNNLS. [2022.12] I am selected for the Rising Stars in AI Symposium 2023 organized by KAUST AI initiative. [2022.10] I obtain the ByteDance Scholarship. [2022.10] Our BiPointNet (ICLR'21) integrated into Amazon's Deep Graph Library (DGL). [2022.09] One first-authored paper for model binarization is accepted by IJCV. [2022.07] I obtain the China Scholarship Council (CSC) scholarship. |
[Workshop@CVPR2023] I am co-organizing the challenge on The 3rd workshop of Adversarial Machine Learning on Computer Vision: Art of Robustness at CVPR 2023. [Workshop@AAAI2023] I am co-organizing the 2nd international workshop on The Practical Deep Learning in the Wild (PracticalDL-23) and Practical AI Challenge at AAAI 2023. |
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You can find the full list on Google Scholar and our group publication page. |
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We present a strong yet efficient binary neural network for KWS, namely BiFSMNv2, pushing it to the real-network accuracy performance. |
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We propose BiBERT, an accurate fully binarized BERT, introducing an efficient Bi-Attention structure and a DMD scheme. BiBERT yields impressive 59.2x and 31.2x saving on FLOPs and model size. |
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We present a novel Distribution-sensitive Information Retention Network (DIR-Net), which improves BNNs by distribution-sensitive optimization without increasing the overhead in the inference process. |
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We present BiFSMN, an accurate and extreme-efficient binary network for KWS, achieving impressive 22.3x speedup and 15.5x storage-saving on edge hardware. |
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We propose a novel Accurate Post-training Quantization framework for Vision Transformer, namely APQ-ViT, which surpasses the existing post-training quantization methods by convincing margins, especially in lower bit settings. |
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We present BiPointNet, the first model binarization approach for efficient deep learning on point clouds, giving an impressive 14.7x speedup and 18.9x storage saving on real-world devices. |
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We proposed Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization in data-free quantization, which obtained significant improvements over various networks and quantization methods. |
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We propose a novel Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients, and we are the first to implement and report advanced BNN speed on edge devices. |
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We presented a comprehensive survey of BNNs. We investigated practical aspects of binary neural networks and gave the evaluation and discussions on different tasks. The challenges may be faced in future research were prospected. |
[2023] Rising Star in AI (awarded by KAUST AI Initiative headed by Prof. Jürgen Schmidhuber) [2022] ByteDance Scholarship [2022] Beihang Top-10 PhD Students Award [2021] The Most Popular Paper in Beijing Area (Field: Graphic and Image) [2021] China National Scholarship [2020] China National Scholarship [2020] Tencent Rhino-Bird Elite [2019] ICPC China National Invitational Contest (Nanchang) - Gold Medal [2018] ACM-ICPC Chinese Collegiate Programming Contest (Ningxia) - Gold Medal |
Organizer of Workshops: AAAI 2022/2023, CVPR 2022, PRCV 2021, VALSE 2022. Reviewer of Journals: IEEE TPAMI/TIP/TNNLS/TMM, Pattern Recognition, etc. Program Committee of Conferences: CVPR 2023, IJCAI 2021/22, ACM MM 2021/22, ICME 2023, etc. |
[2023.02] I am invited to talk about network binarization at KAUST. [Slides] [2022.06] I am invited to host the VALSE Student Webinar & Panel namely "When CV meets NLP". [Media] [2022.01] I am invited to host the VALSE Student Webinar about conference and journal rebuttal. [Media] [2021.07] I am invited to talk about multi-scenario network quantization at J Ventures (将门创投). [Video] [2021.06] I am invited to talk about data-free quantization at Zhidx (智东西公开课). [Video] [2021.05] I am invited to present our DSG (CVPR 2021 oral) at MSRA Tech Talk. [Slides] [2020.04] I am invited to present our IR-Net (CVPR 2020) and survey paper at JD AI Research. [Slides] [Fall 2020] I am the teaching assistant in machine learning course (Beihang University). |
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In my free time, I like playing Chinese folk music, especially string music (Erhu, Zhonghu, etc.). In fact, I am almost a professional Erhu performer. I have studied Erhu supervised by Prof. Zaili Tian, Prof. Yang Gao, and Prof. Qingfu Zhu. I was the vice-president of the Beihang Folk Music Orchestra, here are some of the performance videos of our orchestra [Tencent Video][Bilibili]. |
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