Search Results for author: JieZhang Cao

Found 31 papers, 18 papers with code

Adversarial Learning with Local Coordinate Coding

no code implementations ICML 2018 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).

Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss

no code implementations19 Sep 2018 Yong Guo, Qi Chen, Jian Chen, Junzhou Huang, Yanwu Xu, JieZhang Cao, Peilin Zhao, Mingkui Tan

However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.

Image Super-Resolution

Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching

no code implementations27 Sep 2018 JieZhang Cao, Yong Guo, Langyuan Mo, Peilin Zhao, Junzhou Huang, Mingkui Tan

We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains.

Open-Ended Question Answering Unsupervised Image-To-Image Translation +2

Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors

no code implementations25 Sep 2019 JieZhang Cao, Jincheng Li, Xiping Hu, Peilin Zhao, Mingkui Tan

ii) the $W$-distance of a specific layer to the target distribution tends to decrease along training iterations.

Multi-marginal Wasserstein GAN

3 code implementations NeurIPS 2019 Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation.

Image Generation Translation

Online Adaptive Asymmetric Active Learning with Limited Budgets

1 code implementation18 Nov 2019 Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, JieZhang Cao, Junzhou Huang, Mingkui Tan

In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.

Active Learning Anomaly Detection

Generative Low-bitwidth Data Free Quantization

3 code implementations ECCV 2020 Shoukai Xu, Haokun Li, Bohan Zhuang, Jing Liu, JieZhang Cao, Chuangrun Liang, Mingkui Tan

More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method.

Data Free Quantization

Improving Generative Adversarial Networks with Local Coordinate Coding

1 code implementation28 Jul 2020 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.

Internal Wasserstein Distance for Adversarial Attack and Defense

no code implementations13 Mar 2021 Qicheng Wang, Shuhai Zhang, JieZhang Cao, Jincheng Li, Mingkui Tan, Yang Xiang

Existing attack methods often construct adversarial examples relying on some metrics like the $\ell_p$ distance to perturb samples.

Adversarial Attack Adversarial Defense +2

Learning Defense Transformers for Counterattacking Adversarial Examples

1 code implementation13 Mar 2021 Jincheng Li, JieZhang Cao, Yifan Zhang, Jian Chen, Mingkui Tan

Relying on this, we learn a defense transformer to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs.

Adversarial Defense

LocalViT: Bringing Locality to Vision Transformers

2 code implementations12 Apr 2021 Yawei Li, Kai Zhang, JieZhang Cao, Radu Timofte, Luc van Gool

The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept.

Image Classification

Video Super-Resolution Transformer

1 code implementation12 Jun 2021 JieZhang Cao, Yawei Li, Kai Zhang, Luc van Gool

Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.

Optical Flow Estimation Video Super-Resolution

SwinIR: Image Restoration Using Swin Transformer

9 code implementations23 Aug 2021 Jingyun Liang, JieZhang Cao, Guolei Sun, Kai Zhang, Luc van Gool, Radu Timofte

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Color Image Denoising Grayscale Image Denoising +6

VRT: A Video Restoration Transformer

1 code implementation28 Jan 2022 Jingyun Liang, JieZhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc van Gool

Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.

Deblurring Denoising +7

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

2 code implementations24 Mar 2022 Kai Zhang, Yawei Li, Jingyun Liang, JieZhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc van Gool

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.

Image Denoising Image-to-Image Translation

Towards Lightweight Super-Resolution with Dual Regression Learning

2 code implementations16 Jul 2022 Yong Guo, Jingdong Wang, Qi Chen, JieZhang Cao, Zeshuai Deng, Yanwu Xu, Jian Chen, Mingkui Tan

Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space.

Image Super-Resolution Model Compression +1

Towards Interpretable Video Super-Resolution via Alternating Optimization

1 code implementation21 Jul 2022 JieZhang Cao, Jingyun Liang, Kai Zhang, Wenguan Wang, Qin Wang, Yulun Zhang, Hao Tang, Luc van Gool

These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.

Deblurring Space-time Video Super-resolution +2

Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor

no code implementations23 Mar 2023 Haijin Zeng, Kai Feng, JieZhang Cao, Shaoguang Huang, Yongqiang Zhao, Hiep Luong, Jan Aelterman, Wilfried Philips

DJRD includes a newly designed Quad Bayer remosaicing (QB-Re) block, integrated denoising modules based on Swin-transformer and multi-scale wavelet transform.

Denoising

MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing

no code implementations23 Mar 2023 Haijin Zeng, Kai Feng, Shaoguang Huang, JieZhang Cao, Yongyong Chen, Hongyan zhang, Hiep Luong, Wilfried Philips

The advantage of Maformer is that it can leverage the MSFA information and non-local dependencies present in the data.

Demosaicking

Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising

no code implementations6 May 2023 Haijin Zeng, JieZhang Cao, Kai Feng, Shaoguang Huang, Hongyan zhang, Hiep Luong, Wilfried Philips

However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning.

Hyperspectral Image Denoising Image Denoising +1

Denoising Diffusion Models for Plug-and-Play Image Restoration

2 code implementations15 May 2023 Yuanzhi Zhu, Kai Zhang, Jingyun Liang, JieZhang Cao, Bihan Wen, Radu Timofte, Luc van Gool

Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.

Deblurring Denoising +4

DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model

no code implementations19 Nov 2023 Zhenghao Pan, Haijin Zeng, JieZhang Cao, Kai Zhang, Yongyong Chen

Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time.

Denoising

Deep Equilibrium Diffusion Restoration with Parallel Sampling

1 code implementation20 Nov 2023 JieZhang Cao, Yue Shi, Kai Zhang, Yulun Zhang, Radu Timofte, Luc van Gool

Due to the inherent property of diffusion models, most of these methods need long serial sampling chains to restore HQ images step-by-step.

Image Restoration

Cannot find the paper you are looking for? You can Submit a new open access paper.