Longguang Wang

research

∙ 08/18/2023

Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos

We propose a unified point cloud video self-supervised learning framewor...

0 Xiaoxiao Sheng, et al. ∙

research

∙ 08/18/2023

Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos

Recently, the community has made tremendous progress in developing effec...

0 Zhiqiang Shen, et al. ∙

research

∙ 05/06/2023

PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos

Self-supervised learning can extract representations of good quality fro...

0 Zhiqiang Shen, et al. ∙

research

∙ 04/20/2023

NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

In this report, we summarize the first NTIRE challenge on light field (L...

0 Yingqian Wang, et al. ∙

research

∙ 04/10/2023

Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection aims at separating ...

0 Boyang Li, et al. ∙

research

∙ 02/16/2023

Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution

Exploiting spatial-angular correlation is crucial to light field (LF) im...

0 Zhengyu Liang, et al. ∙

research

∙ 07/19/2022

Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution

Self-supervised cross-modal super-resolution (SR) can overcome the diffi...

0 Xiaoyu Dong, et al. ∙

research

∙ 06/13/2022

Learning a Degradation-Adaptive Network for Light Field Image Super-Resolution

Recent years have witnessed the great advances of deep neural networks (...

3 Yingqian Wang, et al. ∙

research

∙ 04/20/2022

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

In this paper, we summarize the 1st NTIRE challenge on stereo image supe...

7 Longguang Wang, et al. ∙

research

∙ 03/03/2022

Occlusion-Aware Cost Constructor for Light Field Depth Estimation

Matching cost construction is a key step in light field (LF) depth estim...

6 Yingqian Wang, et al. ∙

research

∙ 02/22/2022

Disentangling Light Fields for Super-Resolution and Disparity Estimation

Light field (LF) cameras record both intensity and directions of light r...

7 Yingqian Wang, et al. ∙

research

∙ 01/04/2022

MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution

Infrared small target super-resolution (SR) aims to recover reliable and...

4 Xinyi Ying, et al. ∙

research

∙ 08/17/2021

Light Field Image Super-Resolution with Transformers

Light field (LF) image super-resolution (SR) aims at reconstructing high...

0 Zhengyu Liang, et al. ∙

research

∙ 06/01/2021

Dense Nested Attention Network for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection aims at separating ...

9 Boyang Li, et al. ∙

research

∙ 04/01/2021

Unsupervised Degradation Representation Learning for Blind Super-Resolution

Most existing CNN-based super-resolution (SR) methods are developed base...

0 Longguang Wang, et al. ∙

research

∙ 11/07/2020

Symmetric Parallax Attention for Stereo Image Super-Resolution

Although recent years have witnessed the great advances in stereo image ...

8 Yingqian Wang, et al. ∙

research

∙ 09/16/2020

Parallax Attention for Unsupervised Stereo Correspondence Learning

Stereo image pairs encode 3D scene cues into stereo correspondences betw...

0 Longguang Wang, et al. ∙

research

∙ 07/07/2020

Light Field Image Super-Resolution Using Deformable Convolution

Light field (LF) cameras can record scenes from multiple perspectives, a...

0 Yingqian Wang, et al. ∙

research

∙ 06/17/2020

Learning Sparse Masks for Efficient Image Super-Resolution

Current CNN-based super-resolution (SR) methods process all locations eq...

0 Longguang Wang, et al. ∙

research

∙ 04/08/2020

Learning for Scale-Arbitrary Super-Resolution from Scale-Specific Networks

Recently, the performance of single image super-resolution (SR) has been...

4 Longguang Wang, et al. ∙

research

∙ 04/06/2020

Deformable 3D Convolution for Video Super-Resolution

The spatio-temporal information among video sequences is significant for...

0 Xinyi Ying, et al. ∙

research

∙ 01/06/2020

Deep Video Super-Resolution using HR Optical Flow Estimation

Video super-resolution (SR) aims at generating a sequence of high-resolu...

5 Longguang Wang, et al. ∙

research

∙ 12/17/2019

Spatial-Angular Interaction for Light Field Image Super-Resolution

Light field (LF) cameras record both intensity and directions of light r...

3 Yingqian Wang, et al. ∙

research

∙ 12/10/2019

DeOccNet: Learning to See Through Foreground Occlusions in Light Fields

Background objects occluded in some views of a light field (LF) camera c...

25 Yingqian Wang, et al. ∙

research

∙ 03/15/2019

Flickr1024: A Dataset for Stereo Image Super-Resolution

With the popularity of dual cameras in recently released smart phones, a...

0 Yingqian Wang, et al. ∙

research

∙ 03/14/2019

Learning Parallax Attention for Stereo Image Super-Resolution

Stereo image pairs can be used to improve the performance of super-resol...

22 Longguang Wang, et al. ∙

research

∙ 09/23/2018

Learning for Video Super-Resolution through HR Optical Flow Estimation

Video super-resolution (SR) aims to generate a sequence of high-resoluti...

10 Longguang Wang, et al. ∙

research

∙ 08/23/2017

Fast single image super-resolution based on sigmoid transformation

Single image super-resolution aims to generate a high-resolution image f...

0 Longguang Wang, et al. ∙

research

∙ 06/20/2017

Multi-frame image super-resolution with fast upscaling technique

Multi-frame image super-resolution (MISR) aims to fuse information in lo...

0 Longguang Wang, et al. ∙

Longguang Wang

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