Search Results for author: Chong Mou

Found 12 papers, 11 papers with code

DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models

1 code implementation5 Jul 2023 Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang

Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model.

Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

1 code implementation CVPR 2023 Jiechong Song, Chong Mou, Shiqi Wang, Siwei Ma, Jian Zhang

And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block.

Compressive Sensing

Large-capacity and Flexible Video Steganography via Invertible Neural Network

1 code implementation CVPR 2023 Chong Mou, Youmin Xu, Jiechong Song, Chen Zhao, Bernard Ghanem, Jian Zhang

For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN).

T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

2 code implementations16 Feb 2023 Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, XiaoHu Qie

In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly.

Image Generation Style Transfer

TransCL: Transformer Makes Strong and Flexible Compressive Learning

1 code implementation25 Jul 2022 Chong Mou, Jian Zhang

Compressive learning (CL) is an emerging framework that integrates signal acquisition via compressed sensing (CS) and machine learning for inference tasks directly on a small number of measurements.

Image Classification Semantic Segmentation

MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution

1 code implementation10 May 2022 Chong Mou, Yanze Wu, Xintao Wang, Chao Dong, Jian Zhang, Ying Shan

Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner.

Image Restoration Metric Learning +1

Deep Generalized Unfolding Networks for Image Restoration

1 code implementation CVPR 2022 Chong Mou, Qian Wang, Jian Zhang

Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation.

Image Restoration

Robust Invertible Image Steganography

no code implementations CVPR 2022 Youmin Xu, Chong Mou, Yujie Hu, Jingfen Xie, Jian Zhang

Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Gaussian noise, Poisson noise, and lossy compression.

Image Steganography

Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging

1 code implementation ICCV 2021 Zhuoyuan Wu, Jian Zhang, Chong Mou

To better exploit the spatial-temporal correlation among frames and address the problem of information loss between adjacent phases in existing DUNs, we propose to adopt the 3D-CNN prior in our proximal mapping module and develop a novel dense feature map (DFM) strategy, respectively.

Dynamic Attentive Graph Learning for Image Restoration

1 code implementation ICCV 2021 Chong Mou, Jian Zhang, Zhuoyuan Wu

Specifically, we propose an improved graph model to perform patch-wise graph convolution with a dynamic and adaptive number of neighbors for each node.

Demosaicking Graph Learning +1

COLA-Net: Collaborative Attention Network for Image Restoration

2 code implementations10 Mar 2021 Chong Mou, Jian Zhang, Xiaopeng Fan, Hangfan Liu, Ronggang Wang

Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.

CoLA Image Denoising +1

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