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.
|Title of host publication||Computer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers|
|Editors||Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||18|
|Publication status||Published - 2021|
|Event||15th Asian Conference on Computer Vision, ACCV 2020 - Virtual, Online|
Duration: 2020 Nov 30 → 2020 Dec 4
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th Asian Conference on Computer Vision, ACCV 2020|
|Period||20/11/30 → 20/12/4|
Bibliographical noteFunding 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).
© 2021, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)