Search Results for author: Dong Gong

Found 24 papers, 7 papers with code

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration

Memory-Augmented Dynamic Neural Relational Inference

no code implementations ICCV 2021 Dong Gong, Frederic Z. Zhang, Javen Qinfeng Shi, Anton Van Den Hengel

This motivates us to propose a memory-augmented dynamic neural relational inference method, which maintains two associative memory pools: one for the interactive relations and the other for the individual entities.

Trajectory Prediction

COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

no code implementations23 Apr 2020 Qingsen Yan, Bo wang, Dong Gong, Chuan Luo, Wei Zhao, Jianhu Shen, Qinfeng Shi, Shuo Jin, Liang Zhang, Zheng You

Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.

Computed Tomography (CT) Medical Image Segmentation +1

Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory

no code implementations13 Jan 2020 Xin-Yu Zhang, Dong Gong, Jiewei Cao, Chunhua Shen

Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain.

Person Re-Identification

Semi-supervised Learning via Conditional Rotation Angle Estimation

no code implementations9 Jan 2020 Hai-Ming Xu, Lingqiao Liu, Dong Gong

Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.

Self-Supervised Learning

Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation

no code implementations8 Jan 2020 Dong Gong, Wei Sun, Qinfeng Shi, Anton Van Den Hengel, Yanning Zhang

Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs.

Super-Resolution

Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation

1 code implementation29 Jul 2019 Tong Shen, Dong Gong, Wei zhang, Chunhua Shen, Tao Mei

To tackle the unsupervised domain adaptation problem, we explore the possibilities to generate high-quality labels as proxy labels to supervise the training on target data.

Semantic Segmentation Unsupervised Domain Adaptation

An Effective Two-Branch Model-Based Deep Network for Single Image Deraining

no code implementations14 May 2019 Yinglong Wang, Dong Gong, Jie Yang, Qinfeng Shi, Anton Van Den Hengel, Dehua Xie, Bing Zeng

Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.

Autonomous Driving Single Image Deraining

Attention-guided Network for Ghost-free High Dynamic Range Imaging

5 code implementations CVPR 2019 Qingsen Yan, Dong Gong, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang

Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes.

Optical Flow Estimation

Knowledge Adaptation for Efficient Semantic Segmentation

1 code implementation CVPR 2019 Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan

To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.

Knowledge Distillation Semantic Segmentation

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

no code implementations CVPR 2019 Jie Li, Yu Liu, Dong Gong, Qinfeng Shi, Xia Yuan, Chunxia Zhao, Ian Reid

RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).

3D Semantic Scene Completion Scene Labeling

Variational Bayesian Dropout with a Hierarchical Prior

no code implementations CVPR 2019 Yuhang Liu, Wenyong Dong, Lei Zhang, Dong Gong, Qinfeng Shi

Then, we incorporate such a prior into inferring the joint posterior over network weights and the variance in the hierarchical prior, with which both the network training and the dropout rate estimation can be cast into a joint optimization problem.

MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution

no code implementations12 Oct 2018 Dong Gong, Mingkui Tan, Qinfeng Shi, Anton Van Den Hengel, Yanning Zhang

Compared to existing methods, MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization.

Image Deconvolution

Deblurring Natural Image Using Super-Gaussian Fields

no code implementations ECCV 2018 Yuhang Liu, Wenyong Dong, Dong Gong, Lei Zhang, Qinfeng Shi

Existing sparsity-based priors are usually rooted in modeling the response of images to some specific filters (e. g., image gradients), which are insufficient to capture the complicated image structures.

Blind Image Deblurring Image Deblurring

Learning Deep Gradient Descent Optimization for Image Deconvolution

1 code implementation10 Apr 2018 Dong Gong, Zhen Zhang, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Yanning Zhang

Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

Blind Image Deblurring Image Deblurring +1

Self-Paced Kernel Estimation for Robust Blind Image Deblurring

no code implementations ICCV 2017 Dong Gong, Mingkui Tan, Yanning Zhang, Anton Van Den Hengel, Qinfeng Shi

Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the estimation process.

Blind Image Deblurring Image Deblurring

From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur

no code implementations CVPR 2017 Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, Qinfeng Shi

The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.

Blind Image Deconvolution by Automatic Gradient Activation

no code implementations CVPR 2016 Dong Gong, Mingkui Tan, Yanning Zhang, Anton Van Den Hengel, Qinfeng Shi

We show here that a subset of the image gradients are adequate to estimate the blur kernel robustly, no matter the gradient image is sparse or not.

Image Deconvolution

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