Learning Large-Scale Automatic Image Colorization

By Aditya Deshpande, Jason Rock, D.A. Forsyth. 2015

227 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

Learning Visual Representations with Caption Annotations (2020, background): “tasks consist in predicting missing pieces in manipulated images, for instance context prediction [15], colorization [13,36,66], inpainting of missing portions [46], prediction of image rotations [19], spotting artifacts [31], or cross-channel prediction [67].”
Colorful Image Colorization (2016, methodology): “Compared to the state of the art algorithm on this dataset [10], our method produces more perceptually plausible colorization (see also Figures 10 and 11).”
Self-Supervised Learning of Pretext-Invariant Representations (2019, neutral): “Prior pretext tasks include image colorization [9, 30, 38, 39, 86, 87], orientation prediction [20], affine transform prediction [85], predicting contextual image patches [10], reordering image patches [5, 21, 53, 54, 56], counting visual primitives [55], or their combinations [11].”
Scaling and Benchmarking Self-Supervised Visual Representation Learning (2019, background): “Many pretext tasks have been designed for images that exploit their spatial structure [12, 48–50], color information [11, 37, 38, 74], illumination [16], rotation [24] etc.”
Learning Representations for Automatic Colorization (2016, methodology): “Some [5, 7] show promising results on typical scenes (e.”
The Challenges of Continuous Self-Supervised Learning (2022, methodology): “coding [36, 54, 55], inpainting [59] and colorization [18, 34, 86], to methods that learn representations predictive of spatial context [19, 22, 53], temporal context [21, 47, 58, 61, 79, 80], or concurrent modalities like audio [4, 52, 57, 70], text [17, 23, 62] or speech [43, 44].”
Audio-Visual Instance Discrimination with Cross-Modal Agreement (2020, background): “They can use the spatial structure in images [16, 25, 57, 89], color [14, 45, 46, 90], temporal information in videos [18, 20, 29, 37, 49, 54, 58, 64, 85] among”
Real-time user-guided image colorization with learned deep priors (2017, methodology): “Recently, fully automatic methods (Cheng et al. 2015; Deshpande et al. 2015; Iizuka et al. 2016; Isola et al. 2017; Larsson et al. 2016; Zhang et al. 2016) have been proposed.”
Joint Contrastive Learning with Infinite Possibilities (2020, background): “(2(a)), we observe that JCL performs reasonably well when M ′ is in the range of [5,11], which allows for applicable GPU implementation.”
Colorization Transformer (2021, methodology): “Speciﬁcally, cINN (Ardizzone et al., 2019) use conditional normalizing ﬂows (Dinh et al., 2014), VAE-MDN (Deshpande et al., 2017; 2015) and SCC-DC (Messaoud et al., 2018) use conditional VAEs (Kingma & Welling, 2013), and cGAN (Cao et al., 2017) use GANs (Goodfellow et al., 2014) for generative colo”
ClusterFit: Improving Generalization of Visual Representations (2019, background): “…pre-text tasks can either be domain agnostic [5, 6, 29, 45, 60, 61] or exploit domain-speciﬁc information like spatial structure in images [13, 21, 42–44], color [12, 34, 35, 63, 64], illumination [14], temporal structure [16, 25, 37, 39, 40] or a co-occurring modality like sound [2, 3, 9, 19, 46].”
Deep Colorization (2015, methodology): “More recently, Deshpande et al. [20] propose an automatic colorization framework.”
PixColor: Pixel Recursive Colorization (2017, methodology): “This approach has been pursued in several recent papers [5, 7, 9, 15, 17, 20, 42] which leverages the fact that one may obtain unlimited labeled training pairs by converting color images to grayscale.”