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Junyao Hu (胡钧耀)
理想不大，路还很远。
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最后更新于：2024年9月16日 23:00
Last updated on: 2024-09-16 23:00

Welcome

My photo and WeChat

👋 Hi! My name is Junyao Hu (胡钧耀).

🔍 My research interests include deep learning and computer vision, particularly focusing on generative AI for creativity.

🥰 You can contact with me in following ways: GitHub / Email (hujunyao0329@gmail.com) / WeChat (ID: LittleDream_hjy, and the QR code is in the picture above). Please feel free to make any suggestions. Any questions and inquiries about my work and study life are welcome.

🎞️ I am operating my Chinese self-media channels, sharing my scientific research and life experience, and bringing useful knowledge to everyone. You can see me on Bilibili (@JunyaoHu) and WeChat Official Account (Vision AIGC).

📃 More details are shown on my CV (EN/CN) page.

News

2024-09-09 ✒️ Study I went back to my hometown, Enshi, to take a gap year. I am trying to adjust my working status and find a new research position that suits me.

2024-05-05 💼 Activity I attended VALSE 2024 conference at Chongqing, China.

2024-02-27 😋 Accepted A paper was accepted to CVPR 2024, and I was honored as one of Outstanding Reviewers.

2023-09-01 ✒️ Study I started my PhD studying at Nankai University (NKU) under the supervision of Prof. Jufeng Yang.

2023-07-15 ✒️ Study I ended my undergraduate life at China University of Mining and Technology (CUMT), thanks to all teachers and friends around me, especially my parents!

2023-06-10 💼 Activity I attended VALSE 2023 conference at Wuxi, China.

Selected Publications

If you want to view my all publications, click here.

Note: # = Equal Contribution , * = Corresponding Author.

CVPR24 ExtDM: Distribution extrapolation diffusion model for video prediction
Zhicheng Zhang#, Junyao Hu#, Wentao Cheng*, Danda Paudel, Jufeng Yang

TL;DR: We present ExtDM, a new diffusion model that extrapolates video content from current frames by accurately modeling distribution shifts towards future frames.

📘 CVPR 📃 Paper 📃 中译版 📦 Code ⚒️ Project 📊 Poster 📅 Slide 🎞️ Bilibili 🎞️ YouTube

Details
Abstract: Video prediction is a challenging task due to its nature of uncertainty, especially for forecasting a long period. To model the temporal dynamics, advanced methods benefit from the recent success of diffusion models, and repeatedly refine the predicted future frames with 3D spatiotemporal U-Net. However, there exists a gap between the present and future and the repeated usage of U-Net brings a heavy computation burden. To address this, we propose a diffusion-based video prediction method that predicts future frames by extrapolating the present distribution of features, namely ExtDM. Specifically, our method consists of three components: (i) a motion autoencoder conducts a bijection transformation between video frames and motion cues; (ii) a layered distribution adaptor module extrapolates the present features in the guidance of Gaussian distribution; (iii) a 3D U-Net architecture specialized for jointly fusing guidance and features among the temporal dimension by spatiotemporal-window attention. Extensive experiments on five popular benchmarks covering short- and long-term video prediction verify the effectiveness of ExtDM.

BibTex
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@inproceedings{zhang2024ExtDM,
  title={ExtDM: Distribution Extrapolation Diffusion Model for Video Prediction},
  author={Zhang, Zhicheng and Hu, Junyao and Cheng, Wentao and Paudel, Danda and Yang, Jufeng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2024}
}

😅 I’m still working … I can still learn …. Zzz … 😴

Projects

Academic Service

Reviewer

Other

  • Community Contributor
    • Datawhale: Engaged as a Promotion Ambassador , enhancing community outreach and visibility.
    • Mini-Sora: Managed PR and Review processes, ensuring the content quality of the repo.
    • SmartFlow: Crafted in-depth paper interpretation articles as one of the writers.
  • Teaching Assistant
    • High School Student Talent Plan 2024: Guided students through the fundamentals of computer science and assisted them in developing innovative projects.
    • Discrete Mathematics 2021: Summarized and reinforced core course topics and exam challenges through concise review sessions and targeted problem-solving examples.