Free-Form Image Inpainting With Gated Convolution
Jiahui Yu,Zhe Lin,Jimei Yang,Xiaohui Shen,Xin Lu,Thomas S. Huang +5 more
- 22 Oct 2019
- pp 4470-4479
TL;DR: Yu et al. as mentioned in this paper proposed a generative image inpainting system to complete images with free-form mask and guidance, which is based on gated convolutions learned from millions of images without additional labeling efforts.
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Abstract: We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces. Code, demo and models are available at: \url{https://github.com/JiahuiYu/generative_inpainting}.
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Citations
Unbiased Multi-modality Guidance for Image Inpainting
Lawrence A. Hoffman
- 01 Jan 2022
TL;DR: MMT as discussed by the authors proposes an end-to-end multi-modality guided transformer network, including one inpainting branch and two auxiliary branches for semantic segmentation and edge textures.
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Region Normalization for Image Inpainting
TL;DR: Zhang et al. as discussed by the authors proposed a spatial region-wise normalization named Region Normalization (RN), which divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization.
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Mutual Dual-task Generator with Adaptive Attention Fusion for Image Inpainting
TL;DR: Zhang et al. as discussed by the authors proposed a mutual dual-task generator for image inpainting, which consists of a shared encoder and mutual decoders with the bidirectional cross-domain feature denormalization (CFDN) module inside, which hierarchically models the Segmentation-guided image Texture (ST) generation and Texture-guided semantic segmentation (TS).
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Towards counterfactual and contrastive explainability and transparency of DCNN image classifiers
TL;DR: In this article , the authors propose a method for generating interpretable counterfactual and contrastive explanations for deep convolutional neural networks (DCNNs) by identifying the most important filters in the DCNN representing features and concepts that separate the model's decision between classifying the image to the original inferred class or some other specified alter class.
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Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution
Lei Yu,Xuewei Zhang,Yan Chu +2 more
TL;DR: An adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) based on which the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.
References
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger,Philipp Fischer,Thomas Brox +2 more
- 05 Oct 2015
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Squeeze-and-Excitation Networks
Jie Hu,Li Shen,Samuel Albanie,Gang Sun,Enhua Wu +4 more
- 18 Jun 2018
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola,Jun-Yan Zhu,Tinghui Zhou,Alexei A. Efros +3 more
- 21 Jul 2017
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Justin Johnson,Alexandre Alahi,Li Fei-Fei +2 more
- 08 Oct 2016
TL;DR: In this paper, the authors combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image style transfer, where a feedforward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.