Culture-to-Culture Image Translation and User Evaluation
Not on the map. Why?
Abstract (arXiv)
The article introduces the concept of image "culturization," which we define as the process of altering the ``brushstroke of cultural features" that make objects perceived as belonging to a given culture while preserving their functionalities. First, we defined a pipeline for translating objects' images from a source to a target cultural domain based on state-of-the-art Generative Adversarial Networks. Then, we gathered data through an online questionnaire to test four hypotheses concerning the impact of images belonging to different cultural domains on Italian participants. As expected, results depend on individual tastes and preferences: however, they align with our conjecture that some people, during the interaction with an intelligent system, will prefer to be shown images modified to match their cultural background. The study has two main limitations. First, we focussed on the culturization of individual objects instead of complete scenes. However, objects play a crucial role in conveying cultural meanings and can strongly influence how an image is perceived within a specific cultural context. Understanding and addressing object-level translation is a vital step toward achieving more comprehensive scene-level translation in future research. Second, we performed experiments with Italian participants only. We think that there are unique aspects of Italian culture that make it an interesting and relevant case study for exploring the impact of image culturization. Italy is a very culturally conservative society, and Italians have specific sensitivities and expectations regarding the accurate representation of their cultural identity and traditions, which can shape individuals' preferences and inclinations toward certain visual styles, aesthetics, and design choices. As a consequence, we think they are an ideal candidate for a preliminary investigation of image culturization.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 133 references that are in the map. First posted on arXiv 5 Jan 2022.
Builds on
22 of 133 references.
References · 22
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
- 2016Improved Techniques for Training GANscited by 2,938
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
- 2017Unsupervised Image-to-Image Translation Networkscited by 602
- 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
- 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
- 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
- 2018Pros and Cons of GAN Evaluation Measurescited by 224
- 2016Coupled Generative Adversarial Networkscited by 216
10 more
- 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 208
- 2021Image-to-Image Translation: Methods and Applicationscited by 116
- 2019AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networkscited by 77
- 2018Unsupervised Attention-guided Image to Image Translationcited by 58
- 2020Unpaired Image-to-Image Translation using Adversarial Consistency Losscited by 43
- 2018InstaGAN: Instance-aware Image-to-Image Translationcited by 37
- 2018Attention-GAN for Object Transfiguration in Wild Imagescited by 32
- 2020PFA-GAN: Progressive Face Aging with Generative Adversarial Networkcited by 19
- 2017Context-Aware Semantic Inpaintingcited by 6
- 2018Paving the Way for Culturally Competent Robots: a Position Papercited by 3
Built on by
Citing papers per year: 2023: 1.
1 paper.