Search Results for author: Dongwei Ren

Found 21 papers, 18 papers with code

Robust Deep Ensemble Method for Real-world Image Denoising

1 code implementation8 Jun 2022 Pengju Liu, Hongzhi Zhang, Jinghui Wang, Yuzhi Wang, Dongwei Ren, WangMeng Zuo

In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.

Deblurring Image Deblurring +5

Learning Dual-Pixel Alignment for Defocus Deblurring

1 code implementation26 Apr 2022 Yu Li, Yaling Yi, Dongwei Ren, Qince Li, WangMeng Zuo

Generally, DPANet is an encoder-decoder with skip-connections, where two branches with shared parameters in the encoder are employed to extract and align deep features from left and right views, and one decoder is adopted to fuse aligned features for predicting the all-in-focus image.


Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment

no code implementations CVPR 2022 Yue Cao, Zhaolin Wan, Dongwei Ren, Zifei Yan, WangMeng Zuo

Particularly, by treating all labeled data as positive samples, PU learning is leveraged to identify negative samples (i. e., outliers) from unlabeled data.

Image Quality Assessment

Localization Distillation for Object Detection

1 code implementation12 Apr 2022 Zhaohui Zheng, Rongguang Ye, Qibin Hou, Dongwei Ren, Ping Wang, WangMeng Zuo, Ming-Ming Cheng

Second, we introduce the concept of valuable localization region that can aid to selectively distill the classification and localization knowledge for a certain region.

Knowledge Distillation object-detection +1

Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation

1 code implementation9 Mar 2021 Chaohao Xie, Dongwei Ren, Lei Wang, WangMeng Zuo

For learning pseudo mask generator from the auxiliary dataset, we present a bi-level optimization formulation.

Weakly-Supervised Semantic Segmentation

Localization Distillation for Dense Object Detection

2 code implementations CVPR 2022 Zhaohui Zheng, Rongguang Ye, Ping Wang, Dongwei Ren, WangMeng Zuo, Qibin Hou, Ming-Ming Cheng

Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.

Dense Object Detection Knowledge Distillation +1

Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames

1 code implementation ICCV 2021 Wei Shang, Dongwei Ren, Dongqing Zou, Jimmy S. Ren, Ping Luo, WangMeng Zuo

EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods.


Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance

1 code implementation2 Dec 2020 Yu Li, Ming Liu, Yaling Yi, Qince Li, Dongwei Ren, WangMeng Zuo

To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate.

Reflection Removal

Unpaired Learning of Deep Image Denoising

1 code implementation ECCV 2020 Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, WangMeng Zuo

As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set.

Image Denoising Knowledge Distillation +1

Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation

6 code implementations7 May 2020 Zhaohui Zheng, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, QinGhua Hu, WangMeng Zuo

In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.

Instance Segmentation object-detection +2

What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective

1 code implementation CVPR 2020 Qilong Wang, Li Zhang, Banggu Wu, Dongwei Ren, Peihua Li, WangMeng Zuo, QinGhua Hu

Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task.

Instance Segmentation object-detection +2

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

18 code implementations19 Nov 2019 Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, Dongwei Ren

By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e. g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric.

object-detection Object Detection

Neural Blind Deconvolution Using Deep Priors

1 code implementation CVPR 2020 Dongwei Ren, Kai Zhang, Qilong Wang, QinGhua Hu, WangMeng Zuo

To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.

Deblurring Self-Supervised Learning

STAR: A Structure and Texture Aware Retinex Model

1 code implementation16 Jun 2019 Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, Ling Shao

A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.

Low-Light Image Enhancement

Progressive Image Deraining Networks: A Better and Simpler Baseline

3 code implementations CVPR 2019 Dongwei Ren, WangMeng Zuo, QinGhua Hu, Pengfei Zhu, Deyu Meng

To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.

Image Super-Resolution Single Image Deraining +1

Unsupervised Degradation Learning for Single Image Super-Resolution

no code implementations11 Dec 2018 Tianyu Zhao, Wenqi Ren, Changqing Zhang, Dongwei Ren, QinGhua Hu

Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle.

Image Super-Resolution Single Image Super Resolution

Simultaneous Fidelity and Regularization Learning for Image Restoration

1 code implementation12 Apr 2018 Dongwei Ren, WangMeng Zuo, David Zhang, Lei Zhang, Ming-Hsuan Yang

For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.

Denoising Image Deconvolution +1

Understanding Kernel Size in Blind Deconvolution

1 code implementation6 Jun 2017 Li Si-Yao, Dongwei Ren, Qian Yin

In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises.


Discriminative Learning of Iteration-Wise Priors for Blind Deconvolution

no code implementations CVPR 2015 Wangmeng Zuo, Dongwei Ren, Shuhang Gu, Liang Lin, Lei Zhang

The maximum a posterior (MAP)-based blind deconvolution framework generally involves two stages: blur kernel estimation and non-blind restoration.


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