Culture-to-Culture Image Translation and User Evaluation
- URL: http://arxiv.org/abs/2201.01565v6
- Date: Fri, 12 May 2023 14:18:10 GMT
- Title: Culture-to-Culture Image Translation and User Evaluation
- Authors: Giulia Zaino, Carmine Tommaso Recchiuto, and Antonio Sgorbissa
- Abstract summary: The article introduces the concept of image "culturization," which we define as the process of altering the brushstroke of cultural features"
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.
We gathered data through an online questionnaire to test four hypotheses concerning the impact of images belonging to different cultural domains on Italian participants.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models [10.121734731147376]
Vision-language models' performance remains suboptimal on images from non-western cultures.
Various benchmarks have been proposed to test models' cultural inclusivity, but they have limited coverage of cultures.
We introduce the GlobalRG benchmark, comprising two challenging tasks: retrieval across universals and cultural visual grounding.
arXiv Detail & Related papers (2024-06-28T23:28:28Z) - See It from My Perspective: Diagnosing the Western Cultural Bias of Large Vision-Language Models in Image Understanding [78.88461026069862]
Vision-language models (VLMs) can respond to queries about images in many languages.
We present a novel investigation that demonstrates and localizes Western bias in image understanding.
arXiv Detail & Related papers (2024-06-17T15:49:51Z) - Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
We focus on extrinsic evaluation of cultural competence in two text generation tasks.
We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts.
We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
arXiv Detail & Related papers (2024-06-17T14:03:27Z) - How Culturally Aware are Vision-Language Models? [0.8437187555622164]
Images from folklore genres, such as mythology, folk dance, cultural signs, and symbols, are vital to every culture.
Our research compares the performance of four popular vision-language models in identifying culturally specific information in such images.
We propose a new evaluation metric, Cultural Awareness Score (CAS), dedicated to measuring the degree of cultural awareness in image captions.
arXiv Detail & Related papers (2024-05-24T04:45:14Z) - CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection.
It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs.
We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
arXiv Detail & Related papers (2024-05-24T01:49:02Z) - What You Use is What You Get: Unforced Errors in Studying Cultural Aspects in Agile Software Development [2.9418191027447906]
Investigating the influence of cultural characteristics is challenging due to the multi-faceted concept of culture.
Cultural and social aspects are of high importance for their successful use in practice.
arXiv Detail & Related papers (2024-04-25T20:08:37Z) - Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
We present Impressions, a novel dataset through which to investigate the semiotics of images.
We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images.
This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
arXiv Detail & Related papers (2023-10-27T04:30:18Z) - Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions [10.415002561977655]
This research proposes a Cultural Alignment Test (Hoftede's CAT) to quantify cultural alignment using Hofstede's cultural dimension framework.
We quantitatively evaluate large language models (LLMs) against the cultural dimensions of regions like the United States, China, and Arab countries.
Our results quantify the cultural alignment of LLMs and reveal the difference between LLMs in explanatory cultural dimensions.
arXiv Detail & Related papers (2023-08-25T14:50:13Z) - From Culture to Clothing: Discovering the World Events Behind A Century
of Fashion Images [100.20851232528925]
We propose a data-driven approach to identify specific cultural factors affecting the clothes people wear.
Our work is a first step towards a computational, scalable, and easily refreshable approach to link culture to clothing.
arXiv Detail & Related papers (2021-02-02T18:58:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.