5 papers
A Bayesian Proof of the Bernoulli Theorem
Jingbo Liu, Ilias Zadik
We give a new proof of the Bernoulli theorem, conjectured by Talagrand and proved in the seminal work of Bednorz and Latała. Our approach is based on information-theoretic ideas: l…
MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts
Jiancheng Zhao, Xiang Ji, Yifan Zhan +2
Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existing approaches either adopt task-specific end-to-end designs, raising pa…
Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance
Xiang Ji, Guixu Lin, Zhengwei Yin +2
Motion degradation, manifested as blur in global shutter (GS) images or rolling shutter (RS) distortion in RS counterparts, remains a fundamental challenge in computational imaging…
All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception
Jiancheng Zhao, Xiang Ji, Yinqiang Zheng
Efficiently transferring Learned Image Compression (LIC) model from human perception to machine perception is an emerging challenge in vision-centric representation learning. Exist…
Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding
Jiancheng Zhao, Yifan Zhan, Qingtian Zhu +5
Implicit Neural Representations for Videos (NeRV) have emerged as a powerful paradigm for video representation, enabling direct mappings from frame indices to video frames. However…