J
Jingbo Xie
Publications - 10
Citations - 576
Jingbo Xie is an academic researcher. The author has contributed to research in topics: Feature (computer vision) & Deep learning. The author has an hindex of 9, co-authored 10 publications receiving 283 citations.
Papers
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Journal ArticleDOI
Image super-resolution reconstruction based on feature map attention mechanism
Yuantao Chen,Linwu Liu,Volachith Phonevilay,Ke Gu,Runlong Xia,Jingbo Xie,Qian Zhang,Kai Yang +7 more
TL;DR: The evaluating indicator of Peak Signal to Noise Ratio and Structural Similarity Index has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.
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Research of improving semantic image segmentation based on a feature fusion model
TL;DR: The semantic image segmentation based on a feature fusion model with context features layer-by-layer with better mean Intersection Over Union than the state-of-the-art works is proposed.
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Multiscale fast correlation filtering tracking algorithm based on a feature fusion model
TL;DR: A multiscale fast correlation filtering tracking algorithm based on a feature fusion model that exhibits better robustness and improved performance under real‐time conditions in sophisticated scenarios, including scale variation, deformation, fast motion, occlusion, and so on.
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Research on image inpainting algorithm of improved total variation minimization method
TL;DR: In order to solve the issue mismatching and structure disconnecting in exemplar-based image inpainting, an image completion algorithm based on improved total variation minimization method had been proposed in the paper, refer as ETVM.
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The image annotation algorithm using convolutional features from intermediate layer of deep learning
Yuantao Chen,Linwu Liu,Jiajun Tao,Xi Chen,Runlong Xia,Qian Zhang,Jie Xiong,Kai Yang,Jingbo Xie +8 more
TL;DR: This paper proposes an innovative method in which the visual features of the image are presented by the intermediate layer features of deep learning, while semantic concepts are represented by mean vectors of positive samples.