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Zhaoyang Jia
Observed name:
Jia, Zhaoyang
Institutions:
University of Science and Technology of China,
Microsoft Research Asia (China)
Observed institutions:
University of Science and Technology of China,
Microsoft Research Asia (China)
OpenAlex ID:
A5121258103
Efficient Autoregressive Video Diffusion with Dummy Head
2026
·
Guo, Hang
, Jia, Zhaoyang
, et al.
·
arXiv (Cornell University)
·
23
PDF
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
2026
·
Yang, Senqiao
, Zhang, Kaichen
, et al.
·
arXiv (Cornell University)
·
15
PDF
DLF: Extreme Image Compression with Dual-Generative Latent Fusion
2025
·
Naifu Xue
, Zhaoyang Jia
, et al.
·
IEEE/CVF International Conference on Computer Vision (ICCV)
·
9
PDF
CoD-Lite: Real-Time Diffusion-Based Generative Image Compression
2026
·
Jia, Zhaoyang
, Xue, Naifu
, et al.
·
arXiv (Cornell University)
·
7
PDF
An Efficient Streaming Video Understanding Framework with Agentic Control
2026
·
Liu, Jinming
, Huang, Jianguo
, et al.
·
arXiv (Cornell University)
·
5
PDF
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
2026
·
Zhang, Xinjie
, Zhang, Peng
, et al.
·
arXiv (Cornell University)
·
4
PDF
Generative Video Compression with One-Dimensional Latent Representation
2026
·
Zheng, Zihan
, Jia, Zhaoyang
, et al.
·
arXiv (Cornell University)
·
2
PDF
Generative Video Compression with Adaptive Score Distillation
2026
·
Xue, Naifu
, Jia, Zhaoyang
, et al.
·
arXiv (Cornell University)
·
1
PDF
Ultra-Fast Neural Video Compression
2026
·
Li, Jiahao
, Xie, Wenxuan
, et al.
·
arXiv (Cornell University)
·
1
PDF
Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
2026
·
Guo, Zongyu
, He, Jiajun
, et al.
·
arXiv (Cornell University)
·
1
PDF
PULSE: Unlocking Practical Image Compression on Single-Thread CPU
2026
·
Jia, Zhaoyang
, Zhang, Tianyu
, et al.
·
arXiv (Cornell University)
PDF
Generative Latent Coding for Ultra-Low Bitrate Image Compression
2025
·
Jia, Zhaoyang
, Li, Jiahao
, et al.
·
arXiv (Cornell University)
PDF
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Works count:
12
Citation count:
68
H-index:
5
I10-index:
2
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