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Xingxing Xie
Researcher at Northwestern Polytechnical University
Publications - 11
Citations - 573
Xingxing Xie is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 2, co-authored 5 publications receiving 100 citations.
Papers
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Journal ArticleDOI
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
TL;DR: This article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers and discusses the main challenges of remote sensing images classification and survey.
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Feature Enhancement Network for Object Detection in Optical Remote Sensing Images
TL;DR: A novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images is proposed by unifying DAFE and CFE into the framework of Faster R-CNN and is evaluated on two large-scale remote sensing image object detection datasets including DIOR and DOTA.
Posted Content
Anchor-free Oriented Proposal Generator for Object Detection
TL;DR: Wang et al. as mentioned in this paper proposed Anchor-free Oriented Proposal Generator (AOPG) that abandons the horizontal boxes-related operations from the network architecture and produces coarse oriented boxes by coarse location module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals.
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Dual-aligned Oriented Detector
TL;DR: Comprehensive and extensive evaluations on three benchmarks, including DIOR-R, DOTA, and HRSC2016, indicate that the two-stage oriented object detection method, termed Dual-aligned Oriented Detector (DODet), could obtain consistent and substantial gains compared with the baseline method.
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On Improving Bounding Box Representations for Oriented Object Detection
TL;DR: QPDet as mentioned in this paper proposes a simple and effective bounding box representation by drawing inspiration from the polar coordinate system and integrate it into two detection stages to circumvent the boundary discontinuity problem and inconsistency in regression schemes between the two stages.