Papers Colorization
“Colorization” 태그가 달린 논문 367편 · 필터 해제
ColorFool: Semantic Adversarial Colorization
Adversarial attacks that generate small L_p-norm perturbations to mislead classifiers have limited success in black-box settings and with unseen classifiers. These attacks are also not robust to defenses that use denoisi…
Adversarial AttackColorizationDenoisingLanguage-based Colorization of Scene Sketches
Being natural, touchless, and fun-embracing, language-based inputs have been demonstrated effective for various tasks from image generation to literacy education for children. This paper for the first time presents a lan…
ColorizationImage GenerationScene UnderstandingSketchLow Shot Learning with Untrained Neural Networks for Imaging Inverse Problems
Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural n…
Colorizationcompressed sensingAdversarial Colorization Of Icons Based On Structure And Color Conditions
We present a system to help designers create icons that are widely used in banners, signboards, billboards, homepages, and mobile apps. Designers are tasked with drawing contours, whereas our system colorizes contours in…
ColorizationGenerative Adversarial NetworkRelative Pixel Prediction For Autoregressive Image Generation
In natural images, transitions between adjacent pixels tend to be smooth and gradual, a fact that has long been exploited in image compression models based on predictive coding. In contrast, existing neural autoregressiv…
ColorizationImage ColorizationImage CompressionImage Generation+3Conditional Invertible Neural Networks for Guided Image Generation
In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). It combines the purely generative INN mod…
ColorizationImage ColorizationImage GenerationUnsupervised Sketch-to-Photo Synthesis
Humans can envision a realistic photo given a free-hand sketch that is not only spatially imprecise and geometrically distorted but also without colors and visual details. We study unsupervised sketch-to-photo synthesis …
ColorizationData AugmentationDenoisingImage Generation+4MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization
In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available. What about when the data at disposal are scarce? The m…
ColorizationImage ColorizationMeta-LearningPhotorealistic Material Editing Through Direct Image Manipulation
Creating photorealistic materials for light transport algorithms requires carefully fine-tuning a set of material properties to achieve a desired artistic effect. This is typically a lengthy process that involves a train…
ColorizationImage InpaintingImage ManipulationLearning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System
In the monochrome-color dual-lens system, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color ima…
ColorizationImage Colorization By Capsule Networks
In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for im…
Colorizationimage-classificationImage ClassificationImage ColorizationTag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss
Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a qual…
ColorizationLine Art ColorizationTAGFully Automatic Video Colorization with Self-Regularization and Diversity
We present a fully automatic approach to video colorization with self-regularization and diversity. Our model contains a colorization network for video frame colorization and a refinement network for spatiotemporal color…
ColorizationDiversityChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with semantic information. A generative netwo…
ColorizationGuided Image Generation with Conditional Invertible Neural Networks
In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative I…
ColorizationImage ColorizationImage GenerationDeep Exemplar-based Video Colorization
This paper presents the first end-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we int…
ColorizationSemantic correspondenceSaliency Detection With Fully Convolutional Neural Network
Saliency detection is an important task in image processing as it can solve many problems and it usually is the first step in for other processes. Convolutional neural networks have been proved to be very effective on se…
ColorizationSaliency DetectionUsing colorization as a tool for automatic makeup suggestion
Colorization is the method of converting an image in grayscale to a fully color image. There are multiple methods to do the same. Old school methods used machine learning algorithms and optimization techniques to suggest…
ColorizationDeep LearningColoring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, w…
ColorizationFew-Shot LearningTripletColoring With Limited Data: Few-Shot Colorization via Memory Augmented Networks
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, w…
ColorizationFew-Shot LearningTriplet