Emotional Landscape Image Generation Using Generative Adversarial Networks

Chanjong Park, In Kwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We design a deep learning framework that generates landscape images that match a given emotion. We are working on a more challenging approach to generate landscape scenes that do not have main objects making it easier to recognize the emotion. To solve this problem, deep networks based on generative adversarial networks are proposed. A new residual unit called emotional residual unit (ERU) is proposed to better reflect the emotion on training. An affective feature matching loss (AFM-loss) optimized for the emotional image generation is also proposed. This approach produced better images according to the given emotions. To demonstrate performance of the proposed model, a set of experiments including user studies was conducted. The results reveal a higher preference in the new model than the previous ones, demonstrating the production of images suitable for the given emotions. Ablation studies demonstrate that the ERU and AFM-loss enhanced the performance of the model.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
EditorsHiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages573-590
Number of pages18
ISBN (Print)9783030695378
DOIs
Publication statusPublished - 2021
Event15th Asian Conference on Computer Vision, ACCV 2020 - Virtual, Online
Duration: 2020 Nov 302020 Dec 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12625 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Asian Conference on Computer Vision, ACCV 2020
CityVirtual, Online
Period20/11/3020/12/4

Bibliographical note

Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01419) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2C2014622).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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