Partiality and Misconception: Investigating Cultural Representativeness in Text-to-Image Models

Title of the Paper

Partiality and Misconception: Investigating Cultural Representativeness in Text-To-Image Models

Paper Information

• Research Area: Text-to-image generation technology and cross-cultural bias
• Keywords: text-to-image generation, cultural representativeness, cultural bias, cross-cultural analysis, model training data

Research Background and Issues

• Problems or Challenges Identified by the Authors:
  • Current text-to-image (T2I) models exhibit bias and misrepresentation when generating culturally relevant images, potentially leading to unfair global cultural representation.
  • While some studies have focused on biases related to race, gender, and age, research on cultural representativeness remains limited, particularly concerning the fairness and accuracy of global cultural representation.
• Significance:
  • The cultural images generated by these models may exacerbate existing cultural biases in society, further marginalizing disadvantaged cultural groups.
  • Cultural misrepresentation can spread misinformation, lead to misunderstandings, and hinder global cultural understanding and communication.
• Research Motivation and Related Work:
  • Recent generative models, such as DALL-E v2 and Stable Diffusion, have made significant advancements in accuracy and expressiveness, but studies have revealed notable cultural biases in their generated content.
  • The authors aim to delve deeper into these biases, understand their causes, and provide recommendations for improvement.

Proposed Solution

• Proposed Solution:
  • Introduce two dimensions—cultural groups and cultural entities—to quantitatively evaluate the cultural representativeness of T2I models.
  • Propose a hybrid machine-human evaluation method for assessing T2I models.
  • Develop a Universal Cultural Object Grounding Corpus (UCOGC), a benchmark dataset encompassing diverse cultural objects from 30 countries.
• Innovations:
  • Propose a multidimensional cultural representativeness analysis framework to analyze 10 global cultural groups and 9 categories of cultural entities.
  • Extract features from generated images and compare them with real-world image datasets to measure the accuracy of generated content.
• Implementation Steps and Key Techniques:
  • Utilize three T2I models (DALL-E v2, Stable Diffusion v1.5, and v2.1) to generate culturally representative images.
  • Construct text prompts related to specific countries and cultural objects, generating a large number of images across all models for analysis.
  • Employ transfer learning with ResNet and ViT to analyze generation biases in the models.
  • Conduct reliability checks using a hybrid evaluation method, including quantitative metrics such as FID/KID and human evaluation.

Research Findings

• Specific Findings:
  • Defined and introduced the two analytical dimensions of cultural groups and cultural entities.
  • Designed and validated the UCOGC benchmark dataset to evaluate T2I models' performance in cultural representation.
  • Generated and analyzed over 190,000 images, comparing them with the benchmark dataset to reveal significant biases in cultural representation by the models.
• Advantages Over Existing Solutions:
  • Comprehensive coverage of both tangible (e.g., clothing, architecture) and intangible (e.g., performances, festivals) cultural objects.
  • Evaluation methods combine quantitative metrics and human scoring to ensure reliability and alignment with human perception.
• Experimental or Evaluation Results:
  • Unequal cultural distribution: Cultures from underrepresented countries, such as those in South Asia and Africa, are significantly underrepresented compared to their actual population proportions.
  • Diversity discrepancies: Content generated for some countries is overly homogeneous, reducing the richness of cultural expression and potentially reinforcing cultural stereotypes.
  • Quality of generation: More than half of the cultural objects were misrepresented, particularly for specific cultural items—for instance, traditional Chinese clothing like "Tangzhuang" was often misinterpreted as generic "Western suits."
• Limitations and Future Directions:
  • The current dataset does not cover all global cultural objects, and certain cultural groups may still be underrepresented.
  • Future plans include expanding the dataset and involving experts to enhance its comprehensiveness.
  • Extend the analysis to emerging T2I models (e.g., Imagen) to cover a more diverse range of models.

Additional Information

• Published dataset and code resources for further analysis: https://github.com/Hi-2048/chi.git

This study provides a comprehensive evaluation of the bias in cultural representation within text-to-image generation models and offers insightful directions for improving the next generation of models.