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Poster abstract: An efficient edge-assisted mobile system for video photorealistic style transfer

Publication ,  Conference
Li, A; Wu, C; Chen, Y; Ni, B
Published in: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019
November 7, 2019

In the past decade, convolutional neural networks (CNNs) have achieved great practical success in image transformation tasks, including style transfer, semantic segmentation, etc. CNN-based style transfer, which denotes transforming an image into a desired output image according to a user-specified style image, is one of the most popular techniques in image transformation. It has led to to many successful industrial applications with significant commercial impacts, such as Prisma and DeepArt. Figure 1 shows the general workflow of the CNN-based style transfer. Given a content image and a user-specified style image, the content features and style features can be extracted using a pre-trained CNN, and then be merged to generate the stylized image. The CNN model is trained for generating a stylized image that has similar content features as the content image's and similar style features as the style image's. In this example, we can see the content image is captured at a lake in the daytime, while the style image is another similar scene captured at dusk. After performing style transfer, the content image is successfully transformed to the dusky scene while keeping the content unchanged as the content image.

Duke Scholars

Published In

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019

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Publication Date

November 7, 2019

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332 / 333
 

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Li, A., Wu, C., Chen, Y., & Ni, B. (2019). Poster abstract: An efficient edge-assisted mobile system for video photorealistic style transfer. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019 (pp. 332–333). https://doi.org/10.1145/3318216.3364545
Li, A., C. Wu, Y. Chen, and B. Ni. “Poster abstract: An efficient edge-assisted mobile system for video photorealistic style transfer.” In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019, 332–33, 2019. https://doi.org/10.1145/3318216.3364545.
Li A, Wu C, Chen Y, Ni B. Poster abstract: An efficient edge-assisted mobile system for video photorealistic style transfer. In: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019. 2019. p. 332–3.
Li, A., et al. “Poster abstract: An efficient edge-assisted mobile system for video photorealistic style transfer.” Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019, 2019, pp. 332–33. Scopus, doi:10.1145/3318216.3364545.
Li A, Wu C, Chen Y, Ni B. Poster abstract: An efficient edge-assisted mobile system for video photorealistic style transfer. Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019. 2019. p. 332–333.

Published In

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC 2019

DOI

Publication Date

November 7, 2019

Start / End Page

332 / 333