193 research outputs found
Pixelated Semantic Colorization
While many image colorization algorithms have recently shown the capability
of producing plausible color versions from gray-scale photographs, they still
suffer from limited semantic understanding. To address this shortcoming, we
propose to exploit pixelated object semantics to guide image colorization. The
rationale is that human beings perceive and distinguish colors based on the
semantic categories of objects. Starting from an autoregressive model, we
generate image color distributions, from which diverse colored results are
sampled. We propose two ways to incorporate object semantics into the
colorization model: through a pixelated semantic embedding and a pixelated
semantic generator. Specifically, the proposed convolutional neural network
includes two branches. One branch learns what the object is, while the other
branch learns the object colors. The network jointly optimizes a color
embedding loss, a semantic segmentation loss and a color generation loss, in an
end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that
our network, when trained with semantic segmentation labels, produces more
realistic and finer results compared to the colorization state-of-the-art
Multi-task Self-Supervised Visual Learning
We investigate methods for combining multiple self-supervised tasks--i.e.,
supervised tasks where data can be collected without manual labeling--in order
to train a single visual representation. First, we provide an apples-to-apples
comparison of four different self-supervised tasks using the very deep
ResNet-101 architecture. We then combine tasks to jointly train a network. We
also explore lasso regularization to encourage the network to factorize the
information in its representation, and methods for "harmonizing" network inputs
in order to learn a more unified representation. We evaluate all methods on
ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our
results show that deeper networks work better, and that combining tasks--even
via a naive multi-head architecture--always improves performance. Our best
joint network nearly matches the PASCAL performance of a model pre-trained on
ImageNet classification, and matches the ImageNet network on NYU depth
prediction.Comment: Published at ICCV 201
๋ณํ๋ FusionNet์ ์ด์ฉํ ํ์์กฐ ์ด๋ฏธ์ง์ ์์ฐ์ค๋ฌ์ด ์ฑ์
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์์ฐ๊ณผํ๋ํ ํ๋๊ณผ์ ๊ณ์ฐ๊ณผํ์ ๊ณต, 2021. 2. ๊ฐ๋ช
์ฃผ.In this paper, we propose a grayscale image colorizing technique. The colorization task can be divided into three main ways, the Scribble-based method, Exemplar-based method and Fully automatic method. Our proposed method is included in the third one. We use a deep learning model that is widely used in the colorization eld recently. We propose Encoder-Docoder model using Convolutional Neural Networks. In particular, we modify the FusionNet with good performance to suit this purpose.
Also, in order to get better results, we do not use MSE loss function. Instead, we use the loss function suitable for the colorizing purpose. We use a subset of the ImageNet dataset as the training, validation and test dataset. We take some existing methods from Fully automatic Deep Learning method and compared them with our models. Our algorithm is evaluated using a quantitative metric called PSNR (Peak Signal-to-Noise Ratio). In addition, in order to evaluate the results qualitatively, our model was applied to the test dataset and compared with various other models. Our model has better performance both quantitatively and qualitatively than other models. Finally, we apply our model to old black and white photographs.๋ณธ ๋
ผ๋ฌธ์์๋ ํ์์กฐ ์ด๋ฏธ์ง๋ค์ ๋ํ ์ฑ์ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ฑ์ ์์
์ ํฌ๊ฒ Scribble ๊ธฐ๋ฐ ๋ฐฉ๋ฒ, Exemplar ๊ธฐ๋ฐ ๋ฐฉ๋ฒ, ์์ ์๋ ๋ฐฉ๋ฒ์ ์ธ ๊ฐ์ง๋ก ๋๋ ์ ์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ธ ๋ฒ์งธ ๋ฐฉ๋ฒ์ ์ฌ์ฉํ๋ค. ์ต๊ทผ์ ์ฑ์ ๋ถ์ผ์์ ๋๋ฆฌ ์ฌ์ฉ๋๋ ๋ฅ ๋ฌ๋ ๋ชจ๋ธ์ ์ฌ์ฉํ๋ค. Convolutional Neural Networks๋ฅผ ์ด์ฉํ Encoder-Docoder ๋ชจ๋ธ์ ์ ์ํ๋ค. ํนํ ๊ธฐ์กด์ image segmetation ๋ถ์ผ์์ ์ข์ ์ฑ๋ฅ์ ๋ณด์ด๋ FusionNet์ ์๋ ์ฑ์ ๋ชฉ์ ์ ๋ง๊ฒ ๋ค์ํ ๋ฐฉ๋ฒ์ผ๋ก ์์ ํ๋ค. ๋ํ ๋ ๋์ ๊ฒฐ๊ณผ๋ฅผ ์ป๊ธฐ ์ํด MSE ์์ค ํจ์๋ฅผ ์ฌ์ฉํ์ง ์์๋ค. ๋์ , ์ฐ๋ฆฌ๋ ์๋ ์ฑ์ ๋ชฉ์ ์ ์ ํฉํ ์์ค ํจ์๋ฅผ ์ฌ์ฉํ์๋ค.
ImageNet ๋ฐ์ดํฐ์
์ ๋ถ๋ถ ์งํฉ์ ํ๋ จ, ๊ฒ์ฆ ๋ฐ ํ
์คํธ ๋ฐ์ดํฐ์
์ผ๋ก ์ฌ์ฉํ๋ค. ์ฐ๋ฆฌ๋ ์์ ์๋ ๋ฅ ๋ฌ๋ ๋ฐฉ๋ฒ์์ ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๊ฐ์ ธ์ ์ฐ๋ฆฌ์ ๋ชจ๋ธ๊ณผ ๋น๊ตํ๋ค. ์ฐ๋ฆฌ์ ์๊ณ ๋ฆฌ์ฆ์ PSNR (Peak Signal-to-Noise Ratio)์ด๋ผ๋ ์ ๋์ ์งํ๋ฅผ ์ฌ์ฉํ์ฌ ํ๊ฐ๋์๋ค. ๋ํ ๊ฒฐ๊ณผ๋ฅผ ์ ์ฑ์ ์ผ๋ก ํ๊ฐํ๊ธฐ ์ํด ํ
์คํธ ๋ฐ์ดํฐ์
์ ๋ชจ๋ธ์ ์ ์ฉํ์ฌ ๋ค์ํ ๋ชจ๋ธ๊ณผ ๋น๊ตํ๋ค. ๊ทธ ๊ฒฐ๊ณผ ๋ค๋ฅธ ๋ชจ๋ธ์ ๋นํด ์ ์ฑ์ ์ผ๋ก๋, ์ ๋์ ์ผ๋ก๋ ์ข์ ์ฑ๋ฅ์ ๋ณด์๋ค. ๋ง์ง๋ง์ผ๋ก ์ค๋๋ ํ๋ฐฑ ์ฌ์ง๊ณผ ๊ฐ์ ๋ค์ํ ์ ํ์ ์ด๋ฏธ์ง์ ์ ์ฉํ ๊ฒฐ๊ณผ๋ฅผ ์ ์ํ๋ค.Abstract i
1 Introduction 1
2 Related Works 4
2.1 Scribble-based method . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Exemplar-based method . . . . . . . . . . . . . . . . . . . . . 5
2.3 Fully automatic method . . . . . . . . . . . . . . . . . . . . . 6
3 Proposed Method 8
3.1 Method Overview . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Architecture detail . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3.1 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3.2 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3.3 Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Experiments 14
4.1 CIE Lab Color Space . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3 Qualitative Evaluation . . . . . . . . . . . . . . . . . . . . . . 17
4.4 Quantitative Evaluation . . . . . . . . . . . . . . . . . . . . . 18
4.5 Legacy Old image Colorization . . . . . . . . . . . . . . . . . . 20
5 Conclusion 23
The bibliography 24
Abstract (in Korean) 28Maste
ChromaGAN: 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 network
is used to infer the chromaticity of a given grayscale image conditioned to
semantic clues. This network is framed in an adversarial model that learns to
colorize by incorporating perceptual and semantic understanding of color and
class distributions. The model is trained via a fully self-supervised strategy.
Qualitative and quantitative results show the capacity of the proposed method
to colorize images in a realistic way achieving state-of-the-art results.Comment: 8 pages + reference
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
Exemplar-based colorization approaches rely on reference image to provide
plausible colors for target gray-scale image. The key and difficulty of
exemplar-based colorization is to establish an accurate correspondence between
these two images. Previous approaches have attempted to construct such a
correspondence but are faced with two obstacles. First, using luminance
channels for the calculation of correspondence is inaccurate. Second, the dense
correspondence they built introduces wrong matching results and increases the
computation burden. To address these two problems, we propose Semantic-Sparse
Colorization Network (SSCN) to transfer both the global image style and
detailed semantic-related colors to the gray-scale image in a coarse-to-fine
manner. Our network can perfectly balance the global and local colors while
alleviating the ambiguous matching problem. Experiments show that our method
outperforms existing methods in both quantitative and qualitative evaluation
and achieves state-of-the-art performance.Comment: Accepted by ECCV2022; 14 pages, 10 figure
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