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Nan Xue - IEEE Xplore Author Profile

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Author details

Nan Xue

Publications

37

Citations

2,317

Publications by Year

20142023

Co-Authors:

Xiang BaiSong BaiQianyue BaoSerge BelongieYingjia Bu

Show All Co-Authors (76)

Nan Xue

Affiliation

Ant Group

Publication Topics

Feature Maps,
Bounding Box,
Object Detection,
Convolutional Neural Network,
Depth Estimation,
Input Image,
Line Segment,
Semantic Segmentation,
3D Reconstruction,
Aerial Images,
Final Prediction,
Intersection Over Union

Biography

Nan Xue received the BS and PhD degrees from Wuhan University, Wuhan, China, in 2014 and 2020, respectively. He is currently a research associate professor with the School of Computer Science, Wuhan University. He was a visiting scholar with North Carolina State University from September 2018 to June 2020. His research interests include geometric structure analysis in computer vision.(Based on document published on 6 October 2021).

Publications

37

Citations

2,317

Publications by Year

20142023

Co-Authors:

Xiang Bai
Song Bai
Qianyue Bao
Serge Belongie
Yingjia Bu

Show All Co-Authors (76)

Author's Published Works

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Showing 1-25 of 37 results

Conferences (28)

Journals (9)

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Nan Xue(37)
Gui-Song Xia(26)
Liangpei Zhang(10)
Tianfu Wu(10)
Aranya Chakrabortty(7)
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA(9)
School of Computer Science, Wuhan University, Wuhan, China(6)
Wuhan University, China(4)
LIESMARS, Wuhan University, Wuhan, China(4)
Wuhan University, Wuhan, China(3)
IEEE Transactions on Pattern Analysis and Machine Intelligence(6)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
2014 IEEE Geoscience and Remote Sensing Symposium(2)
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
IEEE(37)
Media(3)
Long Beach, CA, USA(3)
New Orleans, LA, USA(3)
Seattle, WA, USA(3)
Montreal, QC, Canada(2)
Nashville, TN, USA(2)
Feature Maps(10)
Line Segment(9)
Object Detection(8)
Algebraic Riccati Equation(7)
Bounding Box(7)

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Results

Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver

Xianpeng Liu;Ce Zheng;Kelvin Cheng;Nan Xue;Guo-Jun Qi;Tianfu Wu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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The main challenge of monocular 3D object detection is the accurate localization of 3D center. Motivated by a new and strong observation that this challenge can be remedied by a 3D-space local-grid search scheme in an ideal case, we propose a stage-wise approach, which combines the information flow from 2D-to-3D (3D bounding box proposal generation with a single 2D image) and 3D-to-2D (proposal ve...Show More

Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver

Xianpeng Liu;Ce Zheng;Kelvin Cheng;Nan Xue;Guo-Jun Qi;Tianfu Wu

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

Bin Tan;Nan Xue;Tianfu Wu;Gui-Song Xia

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 12 | Journal Article |

Cited by: Papers (7)

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This article studies the challenging two-view 3D reconstruction problem in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs for camera pose estimation. We present a novel Neural One-PlanE RANSAC framework (termed NOPE-SAC in short) that exerts excellent capability of neural networks to learn one-plane pose hypotheses from 3D plane ...Show More

NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

Bin Tan;Nan Xue;Tianfu Wu;Gui-Song Xia

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 12 | Journal Article |

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

Nan Xue;Tianfu Wu;Song Bai;Fu-Dong Wang;Gui-Song Xia;Liangpei Zhang;Philip H. S. Torr

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 12 | Journal Article |

Cited by: Papers (9)

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This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by en...Show More

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

Nan Xue;Tianfu Wu;Song Bai;Fu-Dong Wang;Gui-Song Xia;Liangpei Zhang;Philip H. S. Torr

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 12 | Journal Article |

Depth and DOF Cues Make A Better Defocus Blur Detector

Yuxin Jin;Ming Qian;Jincheng Xiong;Nan Xue;Gui-Song Xia

2023 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Defocus blur detection (DBD) separates in-focus and out-of-focus regions in an image. Previous approaches mistakenly mistook homogeneous areas in focus for defocus blur regions, likely due to not considering the internal factors that cause defocus blur. Inspired by the law of depth, depth of field (DOF), and defocus, we propose an approach called D-DFFNet, which incorporates depth and DOF cues in ...Show More

Depth and DOF Cues Make A Better Defocus Blur Detector

Yuxin Jin;Ming Qian;Jincheng Xiong;Nan Xue;Gui-Song Xia

2023 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2023 | Conference Paper |

Level-S2fM: Structure from Motion on Neural Level Set of Implicit Surfaces

Yuxi Xiao;Nan Xue;Tianfu Wu;Gui-Song Xia

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S2fM, which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established key-point correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configura...Show More

Level-S2fM: Structure from Motion on Neural Level Set of Implicit Surfaces

Yuxi Xiao;Nan Xue;Tianfu Wu;Gui-Song Xia

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

Jian Ding;Nan Xue;Gui-Song Xia;Bernt Schiele;Dengxin Dai

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (13)

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Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation under the domain generalization setting,...Show More

HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

Jian Ding;Nan Xue;Gui-Song Xia;Bernt Schiele;Dengxin Dai

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

HoW-3D: Holistic 3D Wireframe Perception from a Single Image

Wenchao Ma;Bin Tan;Nan Xue;Tianfu Wu;Xianwei Zheng;Gui-Song Xia

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Cited by: Papers (5)

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This paper studies the problem of holistic 3D wireframe perception (HoW-3D), a new task of perceiving both the visible 3D wireframes and the invisible ones from single-view 2D images. As the non-front surfaces of an object cannot be directly observed in a single view, estimating the nonline-of-sight (NLOS) geometries in HoW-3D is a fundamentally challenging problem and remains open in computer vis...Show More

HoW-3D: Holistic 3D Wireframe Perception from a Single Image

Wenchao Ma;Bin Tan;Nan Xue;Tianfu Wu;Xianwei Zheng;Gui-Song Xia

2022 International Conference on 3D Vision (3DV)

Year: 2022 | Conference Paper |

Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation

Nan Xue;Tianfu Wu;Gui-Song Xia;Liangpei Zhang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (23)

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This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specific...Show More

Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation

Nan Xue;Tianfu Wu;Gui-Song Xia;Liangpei Zhang

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Decoupling Zero-Shot Semantic Segmentation

Jian Ding;Nan Xue;Gui–Song Xia;Dengxin Dai

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (107)

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Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not been seen in the training. Existing works formulate ZS3 as a pixel-level zeroshot classification problem, and transfer semantic knowledge from seen classes to unseen ones with the help of language models pre-trained only with texts. While simple, the pixel-level ZS3 formulation shows the limited capability to ...Show More

Decoupling Zero-Shot Semantic Segmentation

Jian Ding;Nan Xue;Gui–Song Xia;Dengxin Dai

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Revisiting Document Image Dewarping by Grid Regularization

Xiangwei Jiang;Rujiao Long;Nan Xue;Zhibo Yang;Cong Yao;Gui-Song Xia

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Cited by: Papers (17)

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This paper addresses the problem of document image dewarping, which aims at eliminating the geometric distortion in document images for document digitization. Instead of designing a better neural network to approximate the optical flow fields between the inputs and outputs, we pursue the best readability by taking the text lines and the document boundaries into account from a constrained optimizat...Show More

Revisiting Document Image Dewarping by Grid Regularization

Xiangwei Jiang;Rujiao Long;Nan Xue;Zhibo Yang;Cong Yao;Gui-Song Xia

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2022 | Conference Paper |

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

Jian Ding;Nan Xue;Gui-Song Xia;Xiang Bai;Wen Yang;Michael Ying Yang;Serge Belongie;Jiebo Luo;Mihai Datcu;Marcello Pelillo;Liangpei Zhang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 11 | Journal Article |

Cited by: Papers (243)

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In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird’s-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we prese...Show More

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

Jian Ding;Nan Xue;Gui-Song Xia;Xiang Bai;Wen Yang;Michael Ying Yang;Serge Belongie;Jiebo Luo;Mihai Datcu;Marcello Pelillo;Liangpei Zhang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 11 | Journal Article |

Unmixing Convolutional Features for Crisp Edge Detection

Linxi Huan;Nan Xue;Xianwei Zheng;Wei He;Jianya Gong;Gui-Song Xia

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 10 | Journal Article |

Cited by: Papers (50)

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This article presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: Feature mixing in edge classification and side mixing during fusing side predictions. The CATS consists of two modules: A novel trac...Show More

Unmixing Convolutional Features for Crisp Edge Detection

Linxi Huan;Nan Xue;Xianwei Zheng;Wei He;Jianya Gong;Gui-Song Xia

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 10 | Journal Article |

Parsing Table Structures in the Wild

Rujiao Long;Wen Wang;Nan Xue;Feiyu Gao;Zhibo Yang;Yongpan Wang;Gui-Song Xia

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (21)

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This paper tackles the problem of table structure parsing (TSP) from images in the wild. In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or oc...Show More

Parsing Table Structures in the Wild

Rujiao Long;Wen Wang;Nan Xue;Feiyu Gao;Zhibo Yang;Yongpan Wang;Gui-Song Xia

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

Bin Tan;Nan Xue;Song Bai;Tianfu Wu;Gui-Song Xia

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (34)

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This paper presents a neural network built upon Transformers, namely PlaneTR, to simultaneously detect and reconstruct planes from a single image. Different from previous methods, PlaneTR jointly leverages the context information and the geometric structures in a sequence-to-sequence way to holistically detect plane instances in one forward pass. Specifically, we represent the geometric structures...Show More

PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

Bin Tan;Nan Xue;Song Bai;Tianfu Wu;Gui-Song Xia

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

LUAI Challenge 2021 on Learning to Understand Aerial Images

Gui-Song Xia;Jian Ding;Ming Qian;Nan Xue;Jiaming Han;Xiang Bai;Michael Ying Yang;Shengyang Li;Serge Belongie;Jiebo Luo;Mihai Datcu;Marcello Pelillo;Liangpei Zhang;Qiang Zhou;Chao-Hui Yu;Kaixuan Hu;Yingjia Bu;Wenming Tan;Zhe Yang;Wei Li;Shang Liu;Jiaxuan Zhao;Tianzhi Ma;Zi-Han Gao;Lingqi Wang;Yi Zuo;Licheng Jiao;Chang Meng;Hao Wang;Jiahao Wang;Yiming Hui;Zhuojun Dong;Jie Zhang;Qianyue Bao;Zixiao Zhang;Fang Liu

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV’2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 [7] and GID-15 [35] datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images....Show More

LUAI Challenge 2021 on Learning to Understand Aerial Images

Gui-Song Xia;Jian Ding;Ming Qian;Nan Xue;Jiaming Han;Xiang Bai;Michael Ying Yang;Shengyang Li;Serge Belongie;Jiebo Luo;Mihai Datcu;Marcello Pelillo;Liangpei Zhang;Qiang Zhou;Chao-Hui Yu;Kaixuan Hu;Yingjia Bu;Wenming Tan;Zhe Yang;Wei Li;Shang Liu;Jiaxuan Zhao;Tianzhi Ma;Zi-Han Gao;Lingqi Wang;Yi Zuo;Licheng Jiao;Chang Meng;Hao Wang;Jiahao Wang;Yiming Hui;Zhuojun Dong;Jie Zhang;Qianyue Bao;Zixiao Zhang;Fang Liu

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2021 | Conference Paper |

Deep Graph Matching under Quadratic Constraint

Quankai Gao;Fudong Wang;Nan Xue;Jin-Gang Yu;Gui-Song Xia

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (22)

Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matching (DGM) methods lies in their ignorance of explicit constraint of graph structures, which may lead the model to be trapped into local minimum in train...Show More

Deep Graph Matching under Quadratic Constraint

Quankai Gao;Fudong Wang;Nan Xue;Jin-Gang Yu;Gui-Song Xia

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

Jiaming Han;Jian Ding;Nan Xue;Gui-Song Xia

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (476)

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Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model the orientatio...Show More

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

Jiaming Han;Jian Ding;Nan Xue;Gui-Song Xia

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Learning Regional Attraction for Line Segment Detection

Nan Xue;Song Bai;Fu-Dong Wang;Gui-Song Xia;Tianfu Wu;Liangpei Zhang;Philip H.S. Torr

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2021 | Volume: 43, Issue: 6 | Journal Article |

Cited by: Papers (24)

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This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AF...Show More

Learning Regional Attraction for Line Segment Detection

Nan Xue;Song Bai;Fu-Dong Wang;Gui-Song Xia;Tianfu Wu;Liangpei Zhang;Philip H.S. Torr

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2021 | Volume: 43, Issue: 6 | Journal Article |

Zero-Assignment Constraint for Graph Matching With Outliers

Fudong Wang;Nan Xue;Jin-Gang Yu;Gui-Song Xia

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (9)

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Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications. To address this issue, we present the zero-assignment constraint (ZAC) for approaching the graph matching problem in the presence of outliers. The underlying idea is to suppress the matchings of outliers by assigning zero-valued vector...Show More

Zero-Assignment Constraint for Graph Matching With Outliers

Fudong Wang;Nan Xue;Jin-Gang Yu;Gui-Song Xia

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Holistically-Attracted Wireframe Parsing

Nan Xue;Tianfu Wu;Song Bai;Fudong Wang;Gui-Song Xia;Liangpei Zhang;Philip H.S. Torr

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (90)

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This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line ...Show More

Holistically-Attracted Wireframe Parsing

Nan Xue;Tianfu Wu;Song Bai;Fudong Wang;Gui-Song Xia;Liangpei Zhang;Philip H.S. Torr

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

A Functional Representation for Graph Matching

Fu-Dong Wang;Nan Xue;Yipeng Zhang;Gui-Song Xia;Marcello Pelillo

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 11 | Journal Article |

Cited by: Papers (18)

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Graph matching is an important and persistent problem in computer vision and pattern recognition for finding node-to-node correspondence between graphs. However, graph matching that incorporates pairwise constraints can be formulated as a quadratic assignment problem (QAP), which is NP-complete and results in intrinsic computational difficulties. This paper presents a functional representation for...Show More

A Functional Representation for Graph Matching

Fu-Dong Wang;Nan Xue;Yipeng Zhang;Gui-Song Xia;Marcello Pelillo

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 11 | Journal Article |

Learning to Calibrate Straight Lines for Fisheye Image Rectification

Zhucun Xue;Nan Xue;Gui-Song Xia;Weiming Shen

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Cited by: Papers (57) | Patents (1)

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This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images. Assuming that the distorted lines generated by fisheye projection should be straight after rectification, we propose a novel deep neural network to impose explicit geometry constraints onto processes of the fisheye lens calibration and the dist...Show More

Learning to Calibrate Straight Lines for Fisheye Image Rectification

Zhucun Xue;Nan Xue;Gui-Song Xia;Weiming Shen

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Jian Ding;Nan Xue;Yang Long;Gui-Song Xia;Qikai Lu

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Cited by: Papers (917)

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Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (Ro...Show More

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Jian Ding;Nan Xue;Yang Long;Gui-Song Xia;Qikai Lu

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Learning Attraction Field Representation for Robust Line Segment Detection

Nan Xue;Song Bai;Fudong Wang;Gui-Song Xia;Tianfu Wu;Liangpei Zhang

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Cited by: Papers (94)

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This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three compone...Show More

Learning Attraction Field Representation for Robust Line Segment Detection

Nan Xue;Song Bai;Fudong Wang;Gui-Song Xia;Tianfu Wu;Liangpei Zhang

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2019 | Conference Paper |

Sensor and Actuator Placement for Large-Scale Systems: A Projection-Based Formulation

Nan Xue;Chengzhi Yuan

2019 American Control Conference (ACC)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

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The problem of sensor and actuator placement aims at achieving near-optimal control performance with only a small number of sensors or actuators. In this paper we present a new formulation and design for the problem of sensor and actuator placement as a structural-constrained H2 optimization. The structural constraint is defined by two projections which indicate the placement of sensors and actuat...Show More

Sensor and Actuator Placement for Large-Scale Systems: A Projection-Based Formulation

Nan Xue;Chengzhi Yuan

2019 American Control Conference (ACC)

Year: 2019 | Conference Paper |

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