Zihui (Sherry) Xue
Hi, I am Zihui Xue (薛子慧), a Ph.D. student at UT Austin, advised by Prof. Kristen Grauman. I am also a visiting researcher at Facebook AI Research. Previously, I'm fortunate to work with Prof. Radu Marculescu on efficient deep learning and Prof. Hang Zhao on multimodal learning. I obtained my bachelor's degree from Fudan University in 2020, where I worked with Prof. Yuedong Xu .
I'm broadly interested in multimodal learning (images, audio, video, language, etc.). My recent research lies in egocentric video learning.
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- [Aug. 2022] Spent a wonderful summer interning at Facebook AI Research (FAIR) 😊
- [Jan. 2022] Co-advise [Cross Inductive Bias Distillation] got accepted by CVPR'22 🎉
- [Jan. 2022] Check out our SUGAR paper on efficient GNN training 🙇
- [Sep. 2021] One paper got accepted by NeurIPS'21 🎉
- [Sep. 2021] One paper got accepted by CoRL'21 🎉
- [Jul. 2021] Two papers got accepted by ICCV'21 (one first author) 🎉
- [Aug. 2020] Start working with Prof. Hang Zhao at Shanghai Qi Zhi Institue, Tsinghua University on multimodal learning 😊
(a) Multimodal Learning and Self-supervised Learning
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The Modality Focusing Hypothesis: On the Blink of Multimodal Knowledge Distillation
Zihui Xue*,
Zhengqi Gao*
Sucheng Ren*,
Hang Zhao
arXiv preprint, 2022
[paper]
When is multimodal knowledge distillation helpful?
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Dynamic Multimodal Fusion
Zihui Xue,
Radu Marculescu
arXiv preprint, 2022
[paper]
Adaptively fuse multimodal data and generate data-dependent forward paths during inference time.
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What Makes Multi-Modal Learning Better than Single (Provably)
Yu Huang,
Chenzhuang Du,
Zihui Xue,
Xuanyao Chen,
Hang Zhao,
Longbo Huang
Conference on Neural Information Processing Systems (NeurIPS), 2021
[paper]
Can multimodal learning provably perform better than unimodal?
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Multimodal Knowledge Expansion
Zihui Xue,
Sucheng Ren,
Zhengqi Gao,
Hang Zhao
International Conference on Computer Vision (ICCV), 2021
[paper]
[website]
A knowledge distillation-based framework to effectively utilize multimodal data without requiring labels.
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On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua,
Wenxiao Wang,
Zihui Xue,
Sucheng Ren,
Yue Wang,
Hang Zhao
International Conference on Computer Vision (ICCV), 2021
(Oral, Acceptance Rate 3.0%)
[paper]
[website]
Reveal the connection between model collapse and feature correlations!
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(b) Efficient Deep Learning
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SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Zihui Xue,
Yuedong Yang,
Mengtian Yang,
Radu Marculescu
arXiv preprint, 2022
[paper]
An efficient GNN training framework that accounts for resource constraints.
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Anytime Depth Estimation with Limited Sensing and Computation Capabilities on Mobile Devices
Yuedong Yang,
Zihui Xue,
Radu Marculescu
Conference on Robot Learning (CoRL), 2021
[paper]
Anytime Depth Estimation with energy-saving 2D LiDARs and monocular cameras.
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Sampling Graphlets of Multiplex Networks: A Restricted Random Walk Approach
Simiao Jiao,
Zihui Xue,
Xiaowei Chen,
Yuedong Xu
ACM Transactions on the Web (TWEB), 2021
[paper]
A random walk approach to estimate the graphlet concentration in multiplex networks.
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