S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Image-to-image Translation via Contour-consistency Networks

Hsiang-Ying Wang, Hsin-Chun Lin, Chih-Hsien Hsia, Natnuntnita Siriphockpirom, Hsien-I Lin, and Yung-Yao Chen

(Received June 30, 2021; Accepted October 6, 2021)

Keywords: image-to-image translation, contour-consistency networks, inconsistency problem, attention feature map

In this paper, a novel framework for image-to-image translation, is which contour-consistency networks are used to solve the problem of inconsistency between the contours of generated and original images, is proposed. The objective of this study was to address the lack of an adequate training set. At the generator end, the original map is sampled by an encoder to obtain the encoder feature map; the attention feature map is then obtained using the attention module. Using the attention feature map, the decoder can ascertain where more conversions are required. The mechanism at the discriminator end is similar to that at the generator end. The map is sampled through an encoder to obtain the encoder feature map and then converted into the attention feature map. Finally, the map is classified by the classifier as real or fake. Experimental results demonstrate the effectiveness of the proposed method.

Corresponding author: Chih-Hsien Hsia, Yung-Yao Chen




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