7 citations · 11 across the 6 of their papers we have counts for
6 papers
A Restoration Network as an Implicit Prior
Yuyang Hu, Mauricio Delbracio, Peyman Milanfar +1
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methods that allows any image re…
Prompt-tuning latent diffusion models for inverse problems
Hyungjin Chung, Jong Chul Ye, Peyman Milanfar +1
We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Existing methods using latent diffusion models for inver…
MULLER: Multilayer Laplacian Resizer for Vision
Zhengzhong Tu, Peyman Milanfar, Hossein Talebi
Image resizing operation is a fundamental preprocessing module in modern computer vision. Throughout the deep learning revolution, researchers have overlooked the potential of alte…
MRET: Multi-resolution Transformer for Video Quality Assessment
Junjie Ke, Tianhao Zhang, Yilin Wang +2
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA ta…
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
Junjie Ke, Keren Ye, Jiahui Yu +3
Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic…
MAXIM: Multi-Axis MLP for Image Processing
Zhengzhong Tu, Hossein Talebi, Han Zhang +4
Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effe…