20 citations · 45 across the 9 of their papers we have counts for
10 papers
Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models
Zhihong Pan, Xin Zhou, Hao Tian
Transferring large amount of high resolution images over limited bandwidth is an important but very challenging task. Compressing images using extremely low bitrates (<0.1 bpp) has…
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation
Zhihong Pan, Xin Zhou, Hao Tian
Diffusion-based text-to-image generation models like GLIDE and DALLE-2 have gained wide success recently for their superior performance in turning complex text inputs into images o…
Effective Invertible Arbitrary Image Rescaling
Zhihong Pan, Baopu Li, Dongliang He +2
Great successes have been achieved using deep learning techniques for image super-resolution (SR) with fixed scales. To increase its real world applicability, numerous models have…
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Zhihong Pan, Baopu Li, Dongliang He +5
Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and dow…
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov, Radu Timofte, Zhilu Zhang +36
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to…
AutoPruning for Deep Neural Network with Dynamic Channel Masking
Baopu Li, Yanwen Fan, Zhihong Pan +1
Modern deep neural network models are large and computationally intensive. One typical solution to this issue is model pruning. However, most current pruning algorithms depend on h…