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您的检索式：作者名="MEI Junjun"
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| 1 | Label Enhancement for Scene Text Detection显示文摘Segmentation-based scene text detection has drawn a great deal of attention,as it can describe the text instance with arbitrary shapes based on its pixel-level prediction.However,most segmentation-based methods suffer from complex post-processing to separate the text instances which are close to each other,resulting in considerable time consumption during the inference procedure.A label enhancement method is proposed to construct two kinds of training labels for segmentation-based scene text detection in this paper.The label distribution learning(LDL)method is used to overcome the problem brought by pure shrunk text labels that might result in suboptimal detection perfor⁃mance.The experimental results on three benchmarks demonstrate that the proposed method can consistently improve the performance with⁃out sacrificing inference speed. | MEI Junjun GUAN Tao TONG Junwen | 2022 | ZTE Communications2022，20，4： | 0 |
| 2 | Label distribution learning for scene text detection显示文摘Recently,segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved texts.However,existing methods usually need complex post-processing stages to process ambiguous labels,i.e.,the labels of the pixels near the text boundary,which may belong to the text or background.In this paper,we present a framework for segmentation-based scene text detection by learning from ambiguous labels.We use the label distribution learning method to process the label ambiguity of text annotation,which achieves a good performance without using additional post-processing stage.Experiments on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection. | Haoyu MA Ningning LU Junjun MEI Tao GUAN Yu ZHANG Xin GENG | 2023 | Frontiers of Computer Science2023，17，6： | 0 |
| 3 | Recent Advances in Video Coding for Machines Standard and Technologies显示文摘To improve the performance of video compression for machine vision analysis tasks,a video coding for machines(VCM)standard working group was established to promote standardization procedures.In this paper,recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases,requirements,evaluation frameworks and corresponding metrics of the VCM standard are given.Then the existing methods are presented,introducing the existing proposals by category and the research progress of the latest VCM conference.Finally,we give conclusions. | ZHANG Qiang MEI Junjun GUAN Tao SUN Zhewen ZHANG Zixiang YU Li | 2024 | ZTE Communications2024，22，1： | 0 |
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