Biography

Chen Zhao is Research Scientist at King Abdullah University of Science and Technology (KAUST), and Leader of the Video Theme in Image and Video Understanding Lab (IVUL) with Prof. Bernard Ghanum. She obtained her Ph.D from Peking University (PKU), advised by Prof. Wen Gao. Her research interests include computer vision, deep learning, image/video understanding, image/video processing.

Interests
  • Video Action Detection
  • Computer Vision
  • Deep Learning
  • Image/video understanding
  • Image/video Processing
Education
  • Ph.D. in Computer Science, 2016

    Peking University, Beijing, China

  • Research Intern, 2016

    National Institute of Informatics, Tokyo, Japan

  • CSC Joint Ph.D. student, 2012

    University of Washington, Seattle, USA

  • B.Eng. in Software Engineering, 2010

    Sichuan University, Chengdu, China

News

2022

[2022-04-18] All Ego4D challenges are live now!

[2022-03-29] Ego4D was accepted to CVPR'22 as ORAL presentation!

[2022-03-29] MAD was accepted to CVPR'22!

2021

[2021-10-15] Ego4D was released and paper on arxiv!

[2021-07-23] VSGN was accepted to ICCV'21!

[2021-05-20] I was recognized by CVPR’21 as Outstanding Reviewer.

2020

[2020-10-13] I presented our work ThumbNet on ACM Multimedia 2020.

[2020-07-29] ThumbNet was accepted to ACM MM'20.

[2020-06-07] We won the 2‑nd place in the HACS’20 Weakly‑supervised action detection Challenge.

[2020-02-27] G-TAD was accepted to CVPR'20!

2019

[2019-10-23] Our paper for YouTube-8M challenge got accepted as oral presentation in ICCV'19 Workshop!

[2019-10-12] We missed the gold medal by only 0.0004 in Kaggle’s 3rd YouTube‑8M Video Understanding Challenge; rank 9/11 out of 283 teams in the public/private leaderboards.

Publications

(2022). R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning. arXiv'22.

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(2022). MAD: A scalable dataset for language grounding in videos from movie audio descriptions. CVPR'22.

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(2022). Ego4D: Around the World in 3,000 Hours of Egocentric Video. CVPR'22.

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(2022). End-to-End Active Speaker Detection. arXiv'22.

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(2022). SegTAD: Precise Temporal Action Detection via Semantic Segmentation. arXiv'22.

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(2022). When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search. arXiv'22.

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(2021). Video Self‑Stitching Graph Network for Temporal Action Localization. ICCV'21.

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(2020). Improve Baseline for Temporal Action Detection: HACS Challenge 2020 Solution of IVUL‑KAUST team. CVPRW'20.

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(2020). Optimization‑Inspired Compact Deep Compressive Sensing. JSTSP.

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(2020). G‑TAD: Sub‑Graph Localization for Temporal Action Detection. CVPR'20.

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(2019). Logistic Regression is Still Alive and Effective: The 3rd YouTube 8M Challenge Solution of the IVUL‑KAUST team. ICCVW'19.

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(2018). CREAM: CNN-REgularized ADMM framework for compressive-sensed image reconstruction. IEEE Access.

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(2018). BoostNet: A Structured Deep Recursive Network to Boost Image Deblocking. VCIP'18.

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(2017). Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior. TCSVT.

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(2017). Video Compressive Sensing Reconstruction via Reweighted Residual Sparsity. TCSVT.

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(2016). CONCOLOR: COnstrained Non-Convex Low-Rank Model for Image Deblocking. TIP.

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(2016). Nonconvex Lp Nuclear Norm based ADMM Framework for Compressive Sensing. DCC'16.

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(2016). Compressive-Sensed Image Coding via Stripe-based DPCM. DCC'16.

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(2015). A Dual Structured-Sparsity Model for Compressive-Sensed Video Reconstruction. VCIP'15.

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(2015). 基于云数据的高效图像编码方法. 计算机学报.

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(2014). Thousand to one: An image compression system via cloud search. MMSP'15.

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(2014). Adaptive intra-refresh for low-delay error-resilient video coding. JVCIR.

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(2014). Video Compressive Sensing via Structured Laplacian Modelling. VCIP'14.

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(2014). Adaptive intra-refresh for low-delay error-resilient video coding0. APSIPA'14.

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(2014). Weakly Supervised Photo Cropping. TMM.

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(2013). Wavelet Inpainting Driven Image Compression via Collaborative Sparsity at Low Bit Rates. ICIP'13.

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(2013). A Highly Effective Error Concealment Method for Whole Frame Loss. ISCAS'13.

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(2012). Image Super-Resolution via Dual-Dictionary Learning and Sparse Representation. ISCAS'12.

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(2012). Exploiting Image Local and Nonlocal Consistency for Mixed Gaussian-Impulse Noise Removal. ICME'12.

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(2012). Compressed Sensing Recovery via Collaborative Sparsity. DCC'12.

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(2012). Image Compressive Sensing Recovery via Collaborative Sparsity. JETCAS.

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Skills

Python

100%

Pytorch

100%

Matlab

70%

C++

70%

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