Shepard Convolutional Neural Networks

By Jimmy S. J. Ren, Li Xu, Qiong Yan, Wenxiu Sun. 2015

190 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021, background): “Then, (Köhler et al., 2014; Ren et al., 2015; Xie et al., 2012) adopt deep neural networks for image completion; (Pathak et al., 2016) first exploits conditional GANs.”
Semantic Image Inpainting with Deep Generative Models (2016, neutral): “Unlike previous hand-crafted matching and editing, learning based methods have shown promising results [27, 38, 33, 22].”
Deep Image Prior (2017, methodology): “Our approach is compared to the method of [27] specifically designed for inpainting.”
Image Inpainting for Irregular Holes Using Partial Convolutions (2018, background): “[22] proposed shepard convolution layer where the same kernel is applied for both feature and mask convolutions.”
Pluralistic Image Completion (2019, background): “Initial works [20, 30] focused on small and thin holes.”
Robust Image Completion via Deep Feature Transformations (2019, methodology): “The learning based methods have shown promising results [65], [66].”
Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting (2019, methodology): “Special convolution operations such as PConv [13] and ShCNN [18] were designed for eliminating the effects caused by the placeholder values in masked regions in an image.”
Removing Rain from Single Images via a Deep Detail Network (2017, background): “The deep CNN has not only achieved success on high-level vision tasks [12, 13] but has also been extended to problems such as image denoising [34, 36], super-resolution [7, 19], reducing artifacts of compression [8], image inpainting [26] and image dehazing [27].”
Low-Light Image Enhancement via a Deep Hybrid Network (2019, methodology): “RNNs [26], [52] have been developed for lowlevel vision tasks.”
Generative Face Completion (2017, background): “Recently Ren et al. [20] learn a convolutional network for inpainting.”
Globally and locally consistent image completion (2017, methodology): “Initially, CNN-based image inpainting approaches were limited to very small and thin masks [Köhler et al. 2014; Ren et al. 2015; Xie et al. 2012].”
Image Inpainting With Learnable Bidirectional Attention Maps (2019, methodology): “Early CNN-based methods [14, 21, 30] are suggested for handling images with small and thin holes.”
Partial Convolution based Padding (2018, methodology): “Inpainting methods like [25, 19] take the hole mask into consideration for reweighting the convolution results.”
Wide-Context Semantic Image Extrapolation (2019, background): “It directly learns semantic features from small-size input, which is both effective and efficient by avoiding bias in common padding and upsampling procedures [33, 40, 26].”
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (2017, methodology): “Other CNN-based SISR methods can be found in [6, 22, 23, 30, 37, 42, 44, 45, 46, 53, 58].”
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal (2016, background): “CNN’s have achieved success on several low level vision tasks, such as image denoising [16], super-resolution [17], [18], image deconvolution [19], image inpainting [20] and image ﬁltering [21].”
Shift-Net: Image Inpainting via Deep Feature Rearrangement (2018, background): “Originally, CNN-based inpainting is con-ﬁned to small and thin masks [21, 34, 45].”
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks (2018, background): “Spatially variant neural networks [26, 19] have been developed for low-level vision tasks.”