Search Results for author: Yulan Guo

Found 38 papers, 25 papers with code

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

no code implementations9 Jan 2022 Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, Yulan Guo

In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.

Semantic Segmentation

Decoupling Makes Weakly Supervised Local Feature Better

no code implementations8 Jan 2022 Kunhong Li, LongguangWang, Li Liu, Qing Ran, Kai Xu, Yulan Guo

Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.

Image Matching

Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

1 code implementation25 Nov 2021 Qian Yin, Qingyong Hu, Hao liu, Feng Zhang, Yingqian Wang, Zaiping Lin, Wei An, Yulan Guo

Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.

Matrix Completion Moving Object Detection +1

Spatial-Temporal Transformer for 3D Point Cloud Sequences

no code implementations19 Oct 2021 Yimin Wei, Hao liu, TingTing Xie, Qiuhong Ke, Yulan Guo

We test the effectiveness our PST2 with two different tasks on point cloud sequences, i. e., 4D semantic segmentation and 3D action recognition.

3D Action Recognition Semantic Segmentation

Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution

1 code implementation9 Aug 2021 Yingqian Wang, Jungang Yang, Yulan Guo, Chao Xiao, Wei An

In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays.

Bilateral Grid Learning for Stereo Matching Networks

no code implementations CVPR 2021 Bin Xu, Yuhua Xu, Xiaoli Yang, Wei Jia, Yulan Guo

In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid.

Robot Navigation Stereo Matching

Dense Nested Attention Network for Infrared Small Target Detection

1 code implementation1 Jun 2021 Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, Yulan Guo

With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained.

Object Detection

Unsupervised Degradation Representation Learning for Blind Super-Resolution

1 code implementation CVPR 2021 Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, Yulan Guo

In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.

Representation Learning Super-Resolution

Deep Learning for Scene Classification: A Survey

no code implementations26 Jan 2021 Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou, Dewen Hu, Matti Pietikäinen, Li Liu

Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision.

General Classification Scene Classification

Symmetric Parallax Attention for Stereo Image Super-Resolution

1 code implementation7 Nov 2020 Yingqian Wang, Xinyi Ying, Longguang Wang, Jungang Yang, Wei An, Yulan Guo

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used.

Occlusion Handling Stereo Image Super-Resolution

A Practical Tutorial on Graph Neural Networks

1 code implementation11 Oct 2020 Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Yulan Guo, Mohammed Bennamoun

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data.

Parallax Attention for Unsupervised Stereo Correspondence Learning

1 code implementation16 Sep 2020 Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An

Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.

Stereo Image Super-Resolution Stereo Matching

Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

3 code implementations5 Aug 2020 Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, Biao Li

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years.

Image Generation

Light Field Image Super-Resolution Using Deformable Convolution

1 code implementation7 Jul 2020 Yingqian Wang, Jungang Yang, Longguang Wang, Xinyi Ying, Tianhao Wu, Wei An, Yulan Guo

In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.

Image Super-Resolution

Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision

no code implementations20 Jun 2020 Haojie Liu, Kang Liao, Chunyu Lin, Yao Zhao, Yulan Guo

Pseudo-LiDAR point cloud interpolation is a novel and challenging task in the field of autonomous driving, which aims to address the frequency mismatching problem between camera and LiDAR.

Autonomous Driving Optical Flow Estimation

Learning Local Features with Context Aggregation for Visual Localization

no code implementations26 May 2020 Siyu Hong, Kunhong Li, Yongcong Zhang, Zhiheng Fu, Mengyi Liu, Yulan Guo

Most existing methods use detect-then-describe or detect-and-describe strategy to learn local features without considering their context information.

Keypoint Detection Visual Localization

Deformable 3D Convolution for Video Super-Resolution

1 code implementation6 Apr 2020 Xinyi Ying, Longguang Wang, Yingqian Wang, Weidong Sheng, Wei An, Yulan Guo

In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR.

Motion Compensation Video Super-Resolution

Deep Learning for 3D Point Clouds: A Survey

3 code implementations27 Dec 2019 Yulan Guo, Hanyun Wang, Qingyong Hu, Hao liu, Li Liu, Mohammed Bennamoun

To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.

3D Object Detection 3D Shape Classification +2

Spatial-Angular Interaction for Light Field Image Super-Resolution

1 code implementation17 Dec 2019 Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo

Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information.

Image Super-Resolution SSIM

DeOccNet: Learning to See Through Foreground Occlusions in Light Fields

1 code implementation10 Dec 2019 Yingqian Wang, Tianhao Wu, Jungang Yang, Longguang Wang, Wei An, Yulan Guo

In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).

PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

no code implementations16 Sep 2019 Haojie Liu, Kang Liao, Chunyu Lin, Yao Zhao, Yulan Guo

In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensors.

Autonomous Driving

Unsupervised Primitive Discovery for Improved 3D Generative Modeling

no code implementations CVPR 2019 Salman H. Khan, Yulan Guo, Munawar Hayat, Nick Barnes

Using the primitive parts for shapes as attributes, a parameterized 3D representation is modeled in the first stage.

3D Shape Generation

Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution

no code implementations15 Mar 2019 Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo

With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs.

Stereo Image Super-Resolution

Learning Parallax Attention for Stereo Image Super-Resolution

1 code implementation CVPR 2019 Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.

Stereo Image Super-Resolution

Learning for Video Super-Resolution through HR Optical Flow Estimation

2 code implementations23 Sep 2018 Longguang Wang, Yulan Guo, Zaiping Lin, Xinpu Deng, Wei An

Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.

Motion Compensation Optical Flow Estimation +1

Learning for Disparity Estimation through Feature Constancy

2 code implementations CVPR 2018 Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, Jianfeng Zhang

The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.

Disparity Estimation Stereo Matching +1

Partial Procedural Geometric Model Fitting for Point Clouds

1 code implementation17 Oct 2016 Zongliang Zhang, Jonathan Li, Yulan Guo, Yangbin Lin, Ming Cheng, Cheng Wang

However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e. g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models.

Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

no code implementations11 Apr 2013 Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, Jianwei Wan

The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets.

3D Object Recognition

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