Yueyu HU, 胡越予

Master Candidate, Peking University

About Me

I am currently pursuing the Master degree with the Institute of Computer Science and Technology, Peking University, advised by Prof. Jiaying Liu at STRUCT. My research interests include computer vision, machine learning and image/video compression. I am also interested in photography, trains and railways, meteorology, science-fiction and computer network development.

Friends

Maybe Useful

Selected Publications
  1. Yueyu Hu, Wenhan Yang, and Jiaying Liu. "Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression", Proc. AAAI Conference on Artificial Intelligence (AAAI), 2020.
    [PDF] [Poster] [Project] [Code] [Spotlight]
  2. Yueyu Hu, Wenhan Yang, Mading Li, and Jiaying Liu. "Progressive Spatial Recurrent Neural Network for Intra Prediction", IEEE Transactions on Multimedia (TMM), 2019.
    [PDF] [Code]
  3. Yueyu Hu, Shuai Yang, Wenhan Yang, Ling-Yu Duan, and Jiaying Liu. "Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach", Proc. IEEE International Conference on Multimedia & Expo (ICME), 2020. (Best Paper Award)
    [PDF] [Project] [Slides]
  4. Shuai Yang, Yueyu Hu, Wenhan Yang, Ling-Yu Duan, and Jiaying Liu. "Towards Coding for Human and Machine Vision: Scalable Face Image Coding", Accepted by IEEE Transactions on Multimedia (TMM), 2020.
  5. Yueyu Hu, Wenhan Yang, Sifeng Xia, Wen-Huang Cheng, and Jiaying Liu. "Enhanced Intra Prediction with Recurrent Neural Network in Video Coding", Proc. Data Compression Conference (DCC), 2018.
    [PDF]
  6. Yueyu Hu, Chunhui Liu, Yanghao Li, Sijie Song and Jiaying Liu. "Temporal Perceptive Network for Skeleton-Based Action Recognition", Proc. British Machine Vision Conference (BMVC), 2017.
    [PDF]
  7. Jiaying Liu, Sijie Song, Chunhui Liu, Yanghao Li, and Yueyu Hu. "A Benchmark Dataset and Comparison Study for Multi-Modal Human Action", ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2019.
    [Dataset]
  8. Haofeng Huang, Wenhan Yang, Yueyu Hu, and Jiaying Liu. "Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment", Proc. Asian Conference on Computer Vision (ACCV), 2020.
    [PDF]
  9. [Full list of publications]

Projects

Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach

In this paper, we come up with a novel image coding framework by leveraging both the compressive and the generative models, to support machine vision and human perception tasks jointly. By introducing advanced generative models, we train a flexible network to reconstruct images from compact feature representations and the reference pixels. Experimental results demonstrate the superiority of our framework in both human visual quality and facial landmark detection, which provide useful evidence on the emerging standardization efforts on MPEG VCM (Video Coding for Machine).

[PDF] [Supplementary] [Project]

Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression

End-to-End learned image compression framework with a coarse-to-fine hyper-prior model, featuring Signal Preserving Hyper Transform and Information Aggregation Reconstruction sub-network. The proposed model achieve superior performance to existing hyper-prior models with context models.

[PDF] [Poster] [Project] [Code] [Spotlight]

Learning End-to-End Lossy Image Compression: A Benchmark

A survey and benchmark of existing end-to-end learned image compression methods.

[arXiv] [Project]

Yueyu HU, 胡越予

Master Candidate, Peking University

About Me

I am currently pursuing the Master degree with the Institute of Computer Science and Technology, Peking University, advised by Prof. Jiaying Liu at STRUCT. My research interests include computer vision, machine learning and image/video compression. I am also interested in photography, trains and railways, meteorology, science-fiction and computer network development.

Selected Publications
  1. Yueyu Hu, Wenhan Yang, and Jiaying Liu. "Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression", Proc. AAAI Conference on Artificial Intelligence (AAAI), 2020.
    [PDF] [Poster] [Project] [Code] [Spotlight]
  2. Yueyu Hu, Wenhan Yang, Mading Li, and Jiaying Liu. "Progressive Spatial Recurrent Neural Network for Intra Prediction", IEEE Transactions on Multimedia (TMM), 2019.
    [PDF] [Code]
  3. Yueyu Hu, Shuai Yang, Wenhan Yang, Ling-Yu Duan, and Jiaying Liu. "Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach", Proc. IEEE International Conference on Multimedia & Expo (ICME), 2020. (Best Paper Award)
    [PDF] [Project] [Slides]
  4. Shuai Yang, Yueyu Hu, Wenhan Yang, Ling-Yu Duan, and Jiaying Liu. "Towards Coding for Human and Machine Vision: Scalable Face Image Coding", Accepted by IEEE Transactions on Multimedia (TMM), 2020.
  5. Yueyu Hu, Wenhan Yang, Sifeng Xia, Wen-Huang Cheng, and Jiaying Liu. "Enhanced Intra Prediction with Recurrent Neural Network in Video Coding", Proc. Data Compression Conference (DCC), 2018.
    [PDF]
  6. Yueyu Hu, Chunhui Liu, Yanghao Li, Sijie Song and Jiaying Liu. "Temporal Perceptive Network for Skeleton-Based Action Recognition", Proc. British Machine Vision Conference (BMVC), 2017.
    [PDF]
  7. Jiaying Liu, Sijie Song, Chunhui Liu, Yanghao Li, and Yueyu Hu. "A Benchmark Dataset and Comparison Study for Multi-Modal Human Action", ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2019.
    [Dataset]
  8. Haofeng Huang, Wenhan Yang, Yueyu Hu, and Jiaying Liu. "Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment", Proc. Asian Conference on Computer Vision (ACCV), 2020.
    [PDF]
  9. [Full list of publications]

Projects

Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach

In this paper, we come up with a novel image coding framework by leveraging both the compressive and the generative models, to support machine vision and human perception tasks jointly. By introducing advanced generative models, we train a flexible network to reconstruct images from compact feature representations and the reference pixels. Experimental results demonstrate the superiority of our framework in both human visual quality and facial landmark detection, which provide useful evidence on the emerging standardization efforts on MPEG VCM (Video Coding for Machine).

[PDF] [Supplementary] [Project]

Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression

End-to-End learned image compression framework with a coarse-to-fine hyper-prior model, featuring Signal Preserving Hyper Transform and Information Aggregation Reconstruction sub-network. The proposed model achieve superior performance to existing hyper-prior models with context models.

[PDF] [Poster] [Project] [Code] [Spotlight]

Learning End-to-End Lossy Image Compression: A Benchmark

A survey and benchmark of existing end-to-end learned image compression methods.

[arXiv] [Project]

Friends

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