Results 231 to 240 of about 131,036 (273)

SegCycle-SPADE: An end-to-end framework for semantic segmentation-based automated extraction and artistic reconstruction of traditional craft patterns using conditional GAN. [PDF]

PLoS One
Huang B, Mo L.
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Fine art image classification and design methods integrating lightweight deep learning. [PDF]

Sci Rep
Ma K, Lee S, Ma X, Chen H.
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Multi-Weather DomainShifter: A Comprehensive Multi-Weather Transfer LLM Agent for Handling Domain Shift in Aerial Image Processing. [PDF]

J Imaging
Wang Y, Wen R, Ishii H, Ohya J.
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Landscape design concept generation combining cultural mapping technology and multimodal modeling. [PDF]

Sci Rep
Wang Y, Xie L, Huang M.
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Using ensemble learning for classifying artistic styles in traditional Chinese woodcuts. [PDF]

Sci Rep
Li K, Chul-Soo K.
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DARE: A large-scale handwritten DAte REcognition system

Dahl CM, Johansen TSD, Sørensen EN, Westermann CE, Wittrock SF.   +4 more
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Neural Font Style Transfer

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017
In this paper, we chose an approach to generate fonts by using neural style transfer. Neural style transfer uses Convolution Neural Networks(CNN) to transfer the style of one image to another. By modifying neural style transfer, we can achieve neural font style transfer.
Gantugs Atarsaikhan, Brian Kenji Iwana, Atsushi Narusawa, Keiji Yanai, Seiichi Uchida   +4 more
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Semantic-aware neural style transfer

Image and Vision Computing, 2019
Abstract This study proposes a semantic-aware style transfer method for resolving semantic mismatch problems in existing algorithms. As the primary focus of this study, the consideration of semantic matching is expected to improve the quality of artistic style transfer.
Joo Hyun Park, Song Park, Hyunjung Shim
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Direction-aware Neural Style Transfer

Proceedings of the 26th ACM international conference on Multimedia, 2018
Neural learning methods have been shown to be effective in style transfer. These methods, which are called NST, aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features of a style image. However, these models using convolutional structures only extract local statistical features of style ...
Hao Wu, Zhengxing Sun, Weihang Yuan
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