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20202023
most citedAIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

20 citations · 57 across the 15 of their papers we have counts for

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10 papers · 1 filter

cs.CV2023

GBSD: Generative Bokeh with Stage Diffusion

Jieren Deng, Xin Zhou, Hao Tian +2

The bokeh effect is an artistic technique that blurs out-of-focus areas in a photograph and has gained interest due to recent developments in text-to-image synthesis and the ubiqui…

cs.CV2023★ 1 cited

Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis

Yansong Gao, Zhihong Pan, Xin Zhou +2

Denoising diffusion probabilistic models (DDPMs) are a class of powerful generative models. The past few years have witnessed the great success of DDPMs in generating high-fidelity…

cs.CV2023

Raising The Limit Of Image Rescaling Using Auxiliary Encoding

Chenzhong Yin, Zhihong Pan, Xin Zhou +2

Normalizing flow models using invertible neural networks (INN) have been widely investigated for successful generative image super-resolution (SR) by learning the transformation be…

cs.CV2022

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…

cs.CV2022★ 2 cited

Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model

Zhiyuan Ren, Zhihong Pan, Xin Zhou +1

We propose a simple and novel method for generating 3D human motion from complex natural language sentences, which describe different velocity, direction and composition of all kin…

cs.CV2022★ 8 cited

Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

Min Zhang, Zhihong Pan, Xin Zhou +1

Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribut…

cs.CV2020★ 16 cited

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…

cs.CV2020★ 1 cited

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…

cs.CV2020★ 20 cited

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Pengxu Wei, Hannan Lu, Radu Timofte +68

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…

cs.CV2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…