no code implementations • CVPR 2023 • Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang, Yulan Guo, Qiong Liu, Xi Zhou
Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost.
1 code implementation • 20 Apr 2023 • Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Radu Timofte, Yulan Guo
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4.
1 code implementation • 10 Apr 2023 • Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo
The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.
1 code implementation • CVPR 2023 • Haiping Wang, YuAn Liu, Zhen Dong, Yulan Guo, Yu-Shen Liu, Wenping Wang, Bisheng Yang
Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses.
no code implementations • CVPR 2023 • Gengxin Liu, Qian Sun, Haibin Huang, Chongyang Ma, Yulan Guo, Li Yi, Hui Huang, Ruizhen Hu
First, although 3D dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale.
no code implementations • 6 Mar 2023 • Yulin He, Wei Chen, Ke Liang, Yusong Tan, Zhengfa Liang, Yulan Guo
Our proposed method, Pseudo-label Correction and Learning (PCL), is extensively evaluated on the MS COCO and PASCAL VOC benchmarks.
1 code implementation • 16 Feb 2023 • Zhengyu Liang, Yingqian Wang, Longguang Wang, Jungang Yang, Shilin Zhou, Yulan Guo
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images.
no code implementations • CVPR 2023 • Sheng Ao, Qingyong Hu, Hanyun Wang, Kai Xu, Yulan Guo
Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization.
no code implementations • CVPR 2023 • Mingtao Feng, Haoran Hou, Liang Zhang, Zijie Wu, Yulan Guo, Ajmal Mian
In-depth understanding of a 3D scene not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them.
1 code implementation • CVPR 2023 • Zhao Jin, Munawar Hayat, Yuwei Yang, Yulan Guo, Yinjie Lei
The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks.
no code implementations • 23 Dec 2022 • Zi Wang, Minglin Chen, Yulan Guo, Zhang Li, Qifeng Yu
Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models.
1 code implementation • 28 Sep 2022 • Tianhao Wu, Boyang Li, Yihang Luo, Yingqian Wang, Chao Xiao, Ting Liu, Jungang Yang, Wei An, Yulan Guo
Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.
2 code implementations • 13 Jun 2022 • Yingqian Wang, Zhengyu Liang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
However, existing DNN-based LF image SR methods are developed on a single fixed degradation (e. g., bicubic downsampling), and thus cannot be applied to super-resolve real LF images with diverse degradations.
no code implementations • 22 May 2022 • Changchong Sheng, Gangyao Kuang, Liang Bai, Chenping Hou, Yulan Guo, Xin Xu, Matti Pietikäinen, Li Liu
Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment.
no code implementations • 25 Apr 2022 • Guangchi Fang, Qingyong Hu, Yiling Xu, Yulan Guo
In addition, we also propose a deep conditional entropy model to estimate the probability distribution of the transformed coefficients, by incorporating temporal context from consecutive point clouds and the motion estimation/compensation modules.
no code implementations • 20 Apr 2022 • Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.
no code implementations • CVPR 2022 • Junhua Xi, Yifei Shi, Yijie Wang, Yulan Guo, Kai Xu
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
1 code implementation • CVPR 2022 • Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, Wen Li
In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data.
1 code implementation • CVPR 2022 • Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo
To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally important to the task of object detection.
1 code implementation • CVPR 2022 • Yuhua Xu, Xiaoli Yang, Yushan Yu, Wei Jia, Zhaobi Chu, Yulan Guo
In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes.
1 code implementation • CVPR 2022 • Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo
Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream.
1 code implementation • CVPR 2022 • Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Wei An, Yulan Guo
Based on the proposed cost constructor, we develop a deep network for LF depth estimation.
no code implementations • 22 Feb 2022 • Yingqian Wang, Longguang Wang, Gaochang Wu, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
1 code implementation • CVPR 2022 • Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Kai Xu
Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds.
no code implementations • 9 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.
1 code implementation • CVPR 2022 • Kunhong Li, Longguang Wang, 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.
Ranked #1 on
Camera Localization
on Aachen Day-Night benchmark
1 code implementation • CVPR 2022 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, Yulan Guo
Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.
1 code implementation • 25 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.
no code implementations • 19 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.
1 code implementation • 9 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.
1 code implementation • ICCV 2021 • Duo Peng, Yinjie Lei, Wen Li, Pingping Zhang, Yulan Guo
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain.
1 code implementation • 6 Jul 2021 • Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham
We study the problem of efficient semantic segmentation of large-scale 3D point clouds.
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.
1 code implementation • 1 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.
2 code implementations • 11 Apr 2021 • Qingyong Hu, Bo Yang, Guangchi Fang, Yulan Guo, Ales Leonardis, Niki Trigoni, Andrew Markham
Labelling point clouds fully is highly time-consuming and costly.
2 code implementations • 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.
no code implementations • 26 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.
1 code implementation • CVPR 2021 • Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo
Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction.
1 code implementation • 7 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.
no code implementations • 18 Oct 2020 • Hong-Xiang Chen, Kunhong Li, Zhiheng Fu, Mengyi Liu, Zonghao Chen, Yulan Guo
A main challenge for tasks on panorama lies in the distortion of objects among images.
1 code implementation • 11 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.
1 code implementation • 16 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.
3 code implementations • 5 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.
1 code implementation • 7 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.
no code implementations • 20 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.
1 code implementation • CVPR 2021 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo
Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
no code implementations • 26 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.
2 code implementations • ICCV 2021 • Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks.
1 code implementation • 6 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.
2 code implementations • 6 Jan 2020 • Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, Wei An
The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.
Ranked #6 on
Video Super-Resolution
on MSU Super-Resolution for Video Compression
(BSQ-rate over ERQA metric)
3 code implementations • 27 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.
1 code implementation • 17 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.
1 code implementation • 10 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).
5 code implementations • CVPR 2020 • Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Ranked #3 on
Semantic Segmentation
on Toronto-3D L002
no code implementations • 16 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.
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.
no code implementations • 15 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.
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.
Ranked #1 on
Image Super-Resolution
on KITTI 2015 - 4x upscaling
no code implementations • 1 Mar 2019 • Yinjie Lei, Ziqin Zhou, Pingping Zhang, Yulan Guo, Zijun Ma, Lingqiao Liu
A sketch based 3D shape retrieval
2 code implementations • 23 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.
Ranked #13 on
Video Super-Resolution
on Vid4 - 4x upscaling
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.
1 code implementation • 17 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.
no code implementations • 11 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.