Search Results for author: Zhihong Pan

Found 19 papers, 2 papers with code

Retrieving Conditions from Reference Images for Diffusion Models

no code implementations5 Dec 2023 Haoran Tang, Xin Zhou, Jieren Deng, Zhihong Pan, Hao Tian, Pratik Chaudhari

Finally, we provide baseline experiment results on new tasks, and conduct ablation studies on the possible structural choices.

Effective Real Image Editing with Accelerated Iterative Diffusion Inversion

no code implementations ICCV 2023 Zhihong Pan, Riccardo Gherardi, Xiufeng Xie, Stephen Huang

Despite all recent progress, it is still challenging to edit and manipulate natural images with modern generative models.


HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision Mitigation

no code implementations ICCV 2023 Xiufeng Xie, Riccardo Gherardi, Zhihong Pan, Stephen Huang

By exploiting the sparsity in the 3D scene to redistribute hash collisions, HollowNeRF improves rendering quality while using a fraction of the parameters of comparable state-of-the-art solutions, leading to a better cost-accuracy trade-off.

GBSD: Generative Bokeh with Stage Diffusion

no code implementations14 Jun 2023 Jieren Deng, Xin Zhou, Hao Tian, Zhihong Pan, Derek Aguiar

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 ubiquity of smart-phone cameras and photo-sharing apps.

Image Generation Image Manipulation +1

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

no code implementations22 Apr 2023 Yansong Gao, Zhihong Pan, Xin Zhou, Le Kang, Pratik Chaudhari

This work analyzes how the backward error affects the diffusion ODEs and the sample quality in DDPMs.


Raising The Limit Of Image Rescaling Using Auxiliary Encoding

no code implementations12 Mar 2023 Chenzhong Yin, Zhihong Pan, Xin Zhou, Le Kang, Paul Bogdan

While the random sampling of latent variable $z$ is useful in generating diverse photo-realistic images, it is not desirable for image rescaling when accurate restoration of the HR image is more important.

Image Super-Resolution

Smooth and Stepwise Self-Distillation for Object Detection

no code implementations9 Mar 2023 Jieren Deng, Xin Zhou, Hao Tian, Zhihong Pan, Derek Aguiar

Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training.

object-detection Object Detection

Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation

no code implementations14 Nov 2022 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 of high quality and wide diversity.

Style Transfer

Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models

no code implementations14 Nov 2022 Zhihong Pan, Xin Zhou, Hao Tian

With the recent success of diffusion models for text-to-image generation, we propose a generative image compression method that demonstrates the potential of saving an image as a short text embedding which in turn can be used to generate high-fidelity images which is equivalent to the original one perceptually.

Image Compression

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

no code implementations22 Oct 2022 Zhiyuan Ren, Zhihong Pan, Xin Zhou, Le Kang

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 kinds of actions.

Denoising Image Generation +1

Effective Invertible Arbitrary Image Rescaling

no code implementations26 Sep 2022 Zhihong Pan, Baopu Li, Dongliang He, Wenhao Wu, Errui Ding

To increase its real world applicability, numerous models have also been proposed to restore SR images with arbitrary scale factors, including asymmetric ones where images are resized to different scales along horizontal and vertical directions.

Image Super-Resolution

Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

no code implementations24 Jul 2022 Min Zhang, Zhihong Pan, Xin Zhou, C. -C. Jay Kuo

Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN).

Image Restoration Image Super-Resolution

Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence

no code implementations CVPR 2022 Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, Errui Ding

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 downscaling degradation kernel.

Image Super-Resolution

AutoPruning for Deep Neural Network with Dynamic Channel Masking

no code implementations22 Oct 2020 Baopu Li, Yanwen Fan, Zhihong Pan, Gang Zhang

In the process of pruning, we utilize a searchable hyperparameter, remaining ratio, to denote the number of channels in each convolution layer, and then a dynamic masking process is proposed to describe the corresponding channel evolution.

AutoML Network Pruning

Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS

no code implementations2 Sep 2020 Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding

With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections.

Image Super-Resolution Neural Architecture Search

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