143 citations · 418 across the 21 of their papers we have counts for
21 papers
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
Rui Li, Dong Gong, Wei Yin +6
Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consis…
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
Junyan Wang, Zhenhong Sun, Yichen Qian +5
3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences,…
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning
Dong Gong, Qingsen Yan, Yuhang Liu +2
Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL ap…
COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
Qingsen Yan, Bo Wang, Dong Gong +7
A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) i…
Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory
Xinyu Zhang, Dong Gong, Jiewei Cao +1
Unsupervised cross-domain person re-identification (Re-ID) aims to adapt the information from the labelled source domain to an unlabelled target domain. Due to the lack of supervis…
Semi-supervised Learning via Conditional Rotation Angle Estimation
Hai-Ming Xu, Lingqiao Liu, Dong Gong
Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress i…
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Dong Gong, Wei Sun, Qinfeng Shi +2
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. The SR methods…
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation
Tong He, Dong Gong, Zhi Tian +1
3D point cloud semantic and instance segmentation is crucial and fundamental for 3D scene understanding. Due to the complex structure, point sets are distributed off balance and di…
Part-Guided Attention Learning for Vehicle Instance Retrieval
Xinyu Zhang, Rufeng Zhang, Jiewei Cao +3
Vehicle instance retrieval often requires one to recognize the fine-grained visual differences between vehicles. Besides the holistic appearance of vehicles which is easily affecte…
Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation
Tong Shen, Dong Gong, Wei Zhang +2
Training a semantic segmentation model requires a large amount of pixel-level annotation, hampering its application at scale. With computer graphics, we can generate almost unlimit…
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
Jie Li, Yu Liu, Dong Gong +4
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…
Attention-guided Network for Ghost-free High Dynamic Range Imaging
Qingsen Yan, Dong Gong, Qinfeng Shi +4
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input lo…
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
Dong Gong, Lingqiao Liu, Vuong Le +4
Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal i…
Knowledge Adaptation for Efficient Semantic Segmentation
Tong He, Chunhua Shen, Zhi Tian +3
Both accuracy and efficiency are of significant importance to the task of semantic segmentation. Existing deep FCNs suffer from heavy computations due to a series of high-resolutio…
Variational Bayesian Dropout with a Hierarchical Prior
Yuhang Liu, Wenyong Dong, Lei Zhang +2
Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weigh…
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
Gerard Snaauw, Dong Gong, Gabriel Maicas +4
Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and management of cardiovascular disease. Deep learning methods have proven to deliver segmentation results co…
MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Dong Gong, Mingkui Tan, Qinfeng Shi +2
Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, ho…
Learning Deep Gradient Descent Optimization for Image Deconvolution
Dong Gong, Zhen Zhang, Qinfeng Shi +3
As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed natur…
An Adaptive Markov Random Field for Structured Compressive Sensing
Suwichaya Suwanwimolkul, Lei Zhang, Dong Gong +4
Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable prop…
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Dong Gong, Jie Yang, Lingqiao Liu +5
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior,…