Decomposed evaluations of geographic disparities in text-to-image models

Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa, Oindrila Saha, Adina Williams, Adriana Romero-Soriano, Megan Richards, Polina Kirichenko, Melissa Hall·June 17, 2024

Summary

This paper introduces Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG), a set of metrics to assess geographic disparities in text-to-image models. The study reveals that while generated images have realistic objects, backgrounds exhibit lower diversity and biases, particularly in regions like Africa and Europe. The research finds that using geographic adjectives in prompts improves background diversity, with a significant improvement in underrepresented regions. The study highlights the need to address biases by focusing on object and background representation, and suggests that Decomposed-DIG can guide future model development for enhanced global representativeness. It also emphasizes the importance of considering both quantitative and qualitative assessments in evaluating these models.

Key findings

12

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address geographic disparities in text-to-image generative models by introducing the Decomposed-DIG benchmark to identify how a widely used LDM contributes to these disparities, particularly through the depiction of backgrounds . This problem is not entirely new, but the paper provides a novel approach to understanding and mitigating geographic disparities in text-to-image generation models by focusing on object and background representation diversity .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to geographic disparities in text-to-image generative models. Specifically, the paper introduces a new set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) to separately measure geographic disparities in the depiction of objects and backgrounds in generated images . The study focuses on understanding patterns of geographic disparities in text-to-image generative models and aims to provide insights into disparities in the realism and representation diversity of objects and backgrounds in generated images . The research seeks to address the limitations of existing measures for evaluating geographic disparities and aims to identify specific examples of disparities in image generation, such as stereotypical background generation in Africa and struggles in generating modern vehicles in Africa .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Decomposed evaluations of geographic disparities in text-to-image models" introduces several novel ideas, methods, and models:

  • Decomposed-DIG Benchmark: The paper introduces the Decomposed-DIG benchmark to analyze geographic disparities in text-to-image generative models. This benchmark allows for a detailed examination of disparities in the depiction of objects and backgrounds in generated images .
  • New Prompting Strategy: The paper proposes a new prompting strategy that uses adjective descriptors like "{regional adjective} {object}" instead of noun descriptors like "{object} in {region}". This new prompting template aims to reduce disparities in image generations. Experimental results show that the adjective-based prompting substantially improves background diversity with minimal impact on object realism or diversity .
  • Identification of Disparities: Using the Decomposed-DIG metrics, the paper identifies specific examples of disparities in generated images, such as stereotypical background generation in Africa, challenges in generating modern vehicles in Africa, and unrealistic placement of objects in outdoor settings. These disparities are pinpointed to provide insights for improvement .
  • Mitigation Strategies: The paper experiments with mitigations through the new prompting strategy, demonstrating up to a 52% improvement in representation diversity of backgrounds for the worst-performing region and a 20% average improvement in generated background diversity. This highlights the potential of the new prompting approach in addressing geographic disparities in text-to-image generation .
  • Fine-Grained Analysis: The work aims to pave the way for more fine-grained analysis and mitigation strategies to address disparities in text-to-image generation. By focusing on specific geographic categorizations and groups, the paper provides insights that can guide efforts towards realism and representation diversity disparity mitigation . The paper "Decomposed evaluations of geographic disparities in text-to-image models" introduces novel characteristics and advantages compared to previous methods:
  • Decomposed-DIG Metrics: The paper introduces the Decomposed-DIG metrics, which allow for a more nuanced analysis of geographic disparities in text-to-image generative models. These metrics decompose indicators into object- and background-indicators, providing a detailed examination of disparities in the depiction of objects and backgrounds in generated images .
  • New Prompting Strategy: The paper proposes a new prompting strategy that utilizes adjective descriptors like "{regional adjective} {object}" instead of noun descriptors like "{object} in {region}". This new prompting template aims to reduce disparities in image generations. Experimental results show that the adjective-based prompting substantially improves background diversity with minimal impact on object realism or diversity .
  • Improved Background Diversity: The new prompting strategy leads to a significant improvement in background diversity, with a 52% enhancement for the worst-performing region and a 20% average improvement overall. This improvement is achieved while maintaining or slightly enhancing background realism and object representation, showcasing the effectiveness of the new prompting approach in addressing geographic disparities in text-to-image generation .
  • Fine-Grained Analysis: The Decomposed-DIG metrics enable a more precise characterization of bias modes in generative models, highlighting disparities such as stereotypical background generation in Africa and challenges in generating modern vehicles in certain regions. This fine-grained analysis provides insights that can inform mitigation strategies to address disparities in text-to-image generation .
  • Mitigation Potential: Through the new prompting strategy, the paper demonstrates that Decomposed-DIG can inform mitigations that lead to significant improvements in representation diversity of backgrounds for the worst-performing region. This indicates the potential of the proposed approach to mitigate geographic disparities in text-to-image generation and pave the way for more accurate and representative image generations across global regions .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of geographic disparities in text-to-image models. Noteworthy researchers in this field include Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa, Oindrila Saha, Adina Williams, Adriana Romero-Soriano, Megan Richards, Polina Kirichenko, and Melissa Hall . These researchers have contributed to identifying and addressing disparities in generated images of different geographic regions, focusing on specific aspects such as object realism and background diversity.

The key to the solution mentioned in the paper is the introduction of a new set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) . This metric allows for the separate measurement of geographic disparities in the depiction of objects and backgrounds in generated images. By using Decomposed-DIG, researchers were able to pinpoint specific examples of disparities, such as stereotypical background generation in Africa, challenges in generating modern vehicles in Africa, and unrealistic placement of objects in outdoor settings. Additionally, the paper introduces a new prompting structure that led to a 52% improvement in representation diversity of backgrounds for the worst-performing region and a 20% average improvement in generated background diversity .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate geographic disparities in text-to-image models through a series of steps :

  1. Decomposed-DIG Benchmark Introduction: The paper introduced the Decomposed-DIG benchmark to identify how a widely used LDM contributes to geographic disparities, particularly through the depiction of backgrounds.
  2. Experimental Setup: The experiments were conducted by students at the University of Massachusetts Amherst on servers at the University of Massachusetts Amherst .
  3. Evaluation Protocol: The paper introduced a decomposed evaluation protocol to disentangle and measure disparities between the target concept and its accompanying background in generated images.
  4. Object and Background Segmentation: The images were segmented into object and background components using the Segment Anything Model (SAM) .
  5. Metrics Used: The experiments utilized metrics such as precision and coverage to measure disparities in realism and diversity in generated images across different geographic regions .
  6. Prompting Strategies: The experiments explored the impact of new prompting strategies on improving background diversity while maintaining object realism and diversity .
  7. Results Analysis: The experiments analyzed the results to understand the disparities between object and background components in the generated images, highlighting the differences in realism and diversity .
  8. Mitigation Strategies: The experiments also investigated the effectiveness of prompting as an early mitigation strategy to reduce disparities in image generations, showing improvements in background diversity with minimal impact on object realism and diversity .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study leverages a set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) to measure geographic disparities in the depiction of objects and backgrounds in generated images . The code for the evaluation metrics and methodologies used in the study is not specified to be open source in the context provided.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study introduces a decomposed evaluation protocol called Decomposed-DIG to disentangle and measure disparities between the target concept and its accompanying background in generated images . This approach allows for a more nuanced understanding of geographic biases in model-generated images, revealing key insights such as higher realism for objects compared to backgrounds in generated images .

Furthermore, the study explores the disparities between geographic regions in the depiction of objects and backgrounds in generated images, highlighting that backgrounds exhibit larger disparities between regions than objects . This detailed analysis helps in identifying bias modes in generative models, such as the lack of paved streets or buildings in backgrounds of images depicting Africa .

Moreover, the experiments conducted in the paper evaluate the impact of different prompting strategies on background diversity. The results show that prompting with region adjectives significantly improves background diversity by 52% for the worst-performing region and by 20% on average, with minimal impact on background realism and object representation . This finding supports the hypothesis that using adjective descriptors in prompts can reduce disparities in image generations .

In conclusion, the experiments and results presented in the paper offer substantial evidence to validate the scientific hypotheses related to geographic disparities in text-to-image models. The detailed analysis provided by the Decomposed-DIG evaluation protocol and the positive impact of different prompting strategies on background diversity support the need for more refined evaluations and informed mitigations to ensure accurate and representative image generations across global regions .


What are the contributions of this paper?

The paper makes several key contributions:

  • Introducing a new set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) to measure geographic disparities in the depiction of objects and backgrounds in generated images .
  • Auditing a widely used latent diffusion model using Decomposed-DIG and finding that generated images depict objects with better realism than backgrounds, with backgrounds in generated images showing larger regional disparities than objects .
  • Pinpointing specific examples of disparities, such as stereotypical background generation in Africa, challenges in generating modern vehicles in Africa, and unrealistic placement of some objects in outdoor settings .
  • Using a new prompting structure enabled by Decomposed-DIG to achieve a 52% improvement in the worst-region and a 20% average improvement in generated background diversity .
  • Identifying that the latent diffusion model contributes to geographic disparities primarily through the depiction of backgrounds, with specific failures like the absence of red sedans for Africa and placing cooking pots outdoors for Europe .
  • Experimenting with a new prompting strategy as an early mitigation attempt, showing that Decomposed-DIG can inform strategies leading to significant improvements in representation diversity of backgrounds for the worst-performing region .

What work can be continued in depth?

Further research can be conducted to delve deeper into the impact of prompting strategies on mitigating geographic disparities in text-to-image models. Specifically, exploring the effectiveness of different prompt templates, such as defining geographic information as adjectives like "European car," in improving background diversity and realism could be a valuable area of study . Additionally, investigating the nuances of how prompting influences the representation diversity of backgrounds for various regions, especially the worst-performing regions, could provide insights into more effective mitigation strategies . This research could contribute to enhancing the accuracy and inclusivity of generative vision models across different global regions.


Introduction
Background
[ ] Emergence of text-to-image models and their impact on visual content generation
[ ] Growing concerns over biases in AI-generated imagery, particularly in representation of diverse regions
Objective
[ ] To develop and propose Decomposed-DIG metrics
[ ] To analyze disparities in image generation by region
[ ] To investigate the effect of geographic prompts on background diversity
[ ] To emphasize the need for global representativeness in model development
Method
Data Collection
[ ] Selection of text-to-image models for evaluation
[ ] Collection of generated images using various prompts, including geographic adjectives
[ ] Diverse dataset of real-world images for comparison
Data Preprocessing
[ ] Image analysis: extracting objects and backgrounds
[ ] Geographic tagging and classification of images
[ ] Calculation of diversity and bias metrics using Decomposed-DIG
Evaluation of Decomposed-DIG
Object Representation
[ ] Quantitative analysis: object diversity across regions
[ ] Qualitative assessment: object distribution and accuracy
Background Representation
[ ] Quantitative: background diversity and biases by geography
[ ] Qualitative: visual patterns and underrepresentation in specific regions
Geographic Adjectives Study
[ ] Effect of prompts on background diversity improvement
[ ] Comparison of bias reduction in underrepresented regions
Implications and Recommendations
Addressing Biases
[ ] Strategies for enhancing object and background representation
[ ] Model fine-tuning and bias mitigation techniques
Future Directions
[ ] Incorporating Decomposed-DIG in model development guidelines
[ ] Importance of continuous monitoring and evaluation
Conclusion
[ ] Summary of findings and contributions
[ ] Call to action for researchers and practitioners to address global representativeness in AI-generated imagery
Basic info
papers
computer vision and pattern recognition
computers and society
machine learning
artificial intelligence
Advanced features
Insights
In which regions do generated images exhibit lower diversity and biases, according to the study?
What are the metrics introduced in the paper for assessing disparities in image generation?
What is the main recommendation from the study regarding biases in text-to-image models and their development?
How does using geographic adjectives in prompts affect background diversity, particularly in underrepresented regions?

Decomposed evaluations of geographic disparities in text-to-image models

Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa, Oindrila Saha, Adina Williams, Adriana Romero-Soriano, Megan Richards, Polina Kirichenko, Melissa Hall·June 17, 2024

Summary

This paper introduces Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG), a set of metrics to assess geographic disparities in text-to-image models. The study reveals that while generated images have realistic objects, backgrounds exhibit lower diversity and biases, particularly in regions like Africa and Europe. The research finds that using geographic adjectives in prompts improves background diversity, with a significant improvement in underrepresented regions. The study highlights the need to address biases by focusing on object and background representation, and suggests that Decomposed-DIG can guide future model development for enhanced global representativeness. It also emphasizes the importance of considering both quantitative and qualitative assessments in evaluating these models.
Mind map
Call to action for researchers and practitioners to address global representativeness in AI-generated imagery
Summary of findings and contributions
Importance of continuous monitoring and evaluation
Incorporating Decomposed-DIG in model development guidelines
Model fine-tuning and bias mitigation techniques
Strategies for enhancing object and background representation
Comparison of bias reduction in underrepresented regions
Effect of prompts on background diversity improvement
Qualitative: visual patterns and underrepresentation in specific regions
Quantitative: background diversity and biases by geography
Qualitative assessment: object distribution and accuracy
Quantitative analysis: object diversity across regions
Calculation of diversity and bias metrics using Decomposed-DIG
Geographic tagging and classification of images
Image analysis: extracting objects and backgrounds
Diverse dataset of real-world images for comparison
Collection of generated images using various prompts, including geographic adjectives
Selection of text-to-image models for evaluation
To emphasize the need for global representativeness in model development
To investigate the effect of geographic prompts on background diversity
To analyze disparities in image generation by region
To develop and propose Decomposed-DIG metrics
Growing concerns over biases in AI-generated imagery, particularly in representation of diverse regions
Emergence of text-to-image models and their impact on visual content generation
Conclusion
Future Directions
Addressing Biases
Geographic Adjectives Study
Background Representation
Object Representation
Data Preprocessing
Data Collection
Objective
Background
Implications and Recommendations
Evaluation of Decomposed-DIG
Method
Introduction
Outline
Introduction
Background
[ ] Emergence of text-to-image models and their impact on visual content generation
[ ] Growing concerns over biases in AI-generated imagery, particularly in representation of diverse regions
Objective
[ ] To develop and propose Decomposed-DIG metrics
[ ] To analyze disparities in image generation by region
[ ] To investigate the effect of geographic prompts on background diversity
[ ] To emphasize the need for global representativeness in model development
Method
Data Collection
[ ] Selection of text-to-image models for evaluation
[ ] Collection of generated images using various prompts, including geographic adjectives
[ ] Diverse dataset of real-world images for comparison
Data Preprocessing
[ ] Image analysis: extracting objects and backgrounds
[ ] Geographic tagging and classification of images
[ ] Calculation of diversity and bias metrics using Decomposed-DIG
Evaluation of Decomposed-DIG
Object Representation
[ ] Quantitative analysis: object diversity across regions
[ ] Qualitative assessment: object distribution and accuracy
Background Representation
[ ] Quantitative: background diversity and biases by geography
[ ] Qualitative: visual patterns and underrepresentation in specific regions
Geographic Adjectives Study
[ ] Effect of prompts on background diversity improvement
[ ] Comparison of bias reduction in underrepresented regions
Implications and Recommendations
Addressing Biases
[ ] Strategies for enhancing object and background representation
[ ] Model fine-tuning and bias mitigation techniques
Future Directions
[ ] Incorporating Decomposed-DIG in model development guidelines
[ ] Importance of continuous monitoring and evaluation
Conclusion
[ ] Summary of findings and contributions
[ ] Call to action for researchers and practitioners to address global representativeness in AI-generated imagery
Key findings
12

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address geographic disparities in text-to-image generative models by introducing the Decomposed-DIG benchmark to identify how a widely used LDM contributes to these disparities, particularly through the depiction of backgrounds . This problem is not entirely new, but the paper provides a novel approach to understanding and mitigating geographic disparities in text-to-image generation models by focusing on object and background representation diversity .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to geographic disparities in text-to-image generative models. Specifically, the paper introduces a new set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) to separately measure geographic disparities in the depiction of objects and backgrounds in generated images . The study focuses on understanding patterns of geographic disparities in text-to-image generative models and aims to provide insights into disparities in the realism and representation diversity of objects and backgrounds in generated images . The research seeks to address the limitations of existing measures for evaluating geographic disparities and aims to identify specific examples of disparities in image generation, such as stereotypical background generation in Africa and struggles in generating modern vehicles in Africa .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Decomposed evaluations of geographic disparities in text-to-image models" introduces several novel ideas, methods, and models:

  • Decomposed-DIG Benchmark: The paper introduces the Decomposed-DIG benchmark to analyze geographic disparities in text-to-image generative models. This benchmark allows for a detailed examination of disparities in the depiction of objects and backgrounds in generated images .
  • New Prompting Strategy: The paper proposes a new prompting strategy that uses adjective descriptors like "{regional adjective} {object}" instead of noun descriptors like "{object} in {region}". This new prompting template aims to reduce disparities in image generations. Experimental results show that the adjective-based prompting substantially improves background diversity with minimal impact on object realism or diversity .
  • Identification of Disparities: Using the Decomposed-DIG metrics, the paper identifies specific examples of disparities in generated images, such as stereotypical background generation in Africa, challenges in generating modern vehicles in Africa, and unrealistic placement of objects in outdoor settings. These disparities are pinpointed to provide insights for improvement .
  • Mitigation Strategies: The paper experiments with mitigations through the new prompting strategy, demonstrating up to a 52% improvement in representation diversity of backgrounds for the worst-performing region and a 20% average improvement in generated background diversity. This highlights the potential of the new prompting approach in addressing geographic disparities in text-to-image generation .
  • Fine-Grained Analysis: The work aims to pave the way for more fine-grained analysis and mitigation strategies to address disparities in text-to-image generation. By focusing on specific geographic categorizations and groups, the paper provides insights that can guide efforts towards realism and representation diversity disparity mitigation . The paper "Decomposed evaluations of geographic disparities in text-to-image models" introduces novel characteristics and advantages compared to previous methods:
  • Decomposed-DIG Metrics: The paper introduces the Decomposed-DIG metrics, which allow for a more nuanced analysis of geographic disparities in text-to-image generative models. These metrics decompose indicators into object- and background-indicators, providing a detailed examination of disparities in the depiction of objects and backgrounds in generated images .
  • New Prompting Strategy: The paper proposes a new prompting strategy that utilizes adjective descriptors like "{regional adjective} {object}" instead of noun descriptors like "{object} in {region}". This new prompting template aims to reduce disparities in image generations. Experimental results show that the adjective-based prompting substantially improves background diversity with minimal impact on object realism or diversity .
  • Improved Background Diversity: The new prompting strategy leads to a significant improvement in background diversity, with a 52% enhancement for the worst-performing region and a 20% average improvement overall. This improvement is achieved while maintaining or slightly enhancing background realism and object representation, showcasing the effectiveness of the new prompting approach in addressing geographic disparities in text-to-image generation .
  • Fine-Grained Analysis: The Decomposed-DIG metrics enable a more precise characterization of bias modes in generative models, highlighting disparities such as stereotypical background generation in Africa and challenges in generating modern vehicles in certain regions. This fine-grained analysis provides insights that can inform mitigation strategies to address disparities in text-to-image generation .
  • Mitigation Potential: Through the new prompting strategy, the paper demonstrates that Decomposed-DIG can inform mitigations that lead to significant improvements in representation diversity of backgrounds for the worst-performing region. This indicates the potential of the proposed approach to mitigate geographic disparities in text-to-image generation and pave the way for more accurate and representative image generations across global regions .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of geographic disparities in text-to-image models. Noteworthy researchers in this field include Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa, Oindrila Saha, Adina Williams, Adriana Romero-Soriano, Megan Richards, Polina Kirichenko, and Melissa Hall . These researchers have contributed to identifying and addressing disparities in generated images of different geographic regions, focusing on specific aspects such as object realism and background diversity.

The key to the solution mentioned in the paper is the introduction of a new set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) . This metric allows for the separate measurement of geographic disparities in the depiction of objects and backgrounds in generated images. By using Decomposed-DIG, researchers were able to pinpoint specific examples of disparities, such as stereotypical background generation in Africa, challenges in generating modern vehicles in Africa, and unrealistic placement of objects in outdoor settings. Additionally, the paper introduces a new prompting structure that led to a 52% improvement in representation diversity of backgrounds for the worst-performing region and a 20% average improvement in generated background diversity .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate geographic disparities in text-to-image models through a series of steps :

  1. Decomposed-DIG Benchmark Introduction: The paper introduced the Decomposed-DIG benchmark to identify how a widely used LDM contributes to geographic disparities, particularly through the depiction of backgrounds.
  2. Experimental Setup: The experiments were conducted by students at the University of Massachusetts Amherst on servers at the University of Massachusetts Amherst .
  3. Evaluation Protocol: The paper introduced a decomposed evaluation protocol to disentangle and measure disparities between the target concept and its accompanying background in generated images.
  4. Object and Background Segmentation: The images were segmented into object and background components using the Segment Anything Model (SAM) .
  5. Metrics Used: The experiments utilized metrics such as precision and coverage to measure disparities in realism and diversity in generated images across different geographic regions .
  6. Prompting Strategies: The experiments explored the impact of new prompting strategies on improving background diversity while maintaining object realism and diversity .
  7. Results Analysis: The experiments analyzed the results to understand the disparities between object and background components in the generated images, highlighting the differences in realism and diversity .
  8. Mitigation Strategies: The experiments also investigated the effectiveness of prompting as an early mitigation strategy to reduce disparities in image generations, showing improvements in background diversity with minimal impact on object realism and diversity .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study leverages a set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) to measure geographic disparities in the depiction of objects and backgrounds in generated images . The code for the evaluation metrics and methodologies used in the study is not specified to be open source in the context provided.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study introduces a decomposed evaluation protocol called Decomposed-DIG to disentangle and measure disparities between the target concept and its accompanying background in generated images . This approach allows for a more nuanced understanding of geographic biases in model-generated images, revealing key insights such as higher realism for objects compared to backgrounds in generated images .

Furthermore, the study explores the disparities between geographic regions in the depiction of objects and backgrounds in generated images, highlighting that backgrounds exhibit larger disparities between regions than objects . This detailed analysis helps in identifying bias modes in generative models, such as the lack of paved streets or buildings in backgrounds of images depicting Africa .

Moreover, the experiments conducted in the paper evaluate the impact of different prompting strategies on background diversity. The results show that prompting with region adjectives significantly improves background diversity by 52% for the worst-performing region and by 20% on average, with minimal impact on background realism and object representation . This finding supports the hypothesis that using adjective descriptors in prompts can reduce disparities in image generations .

In conclusion, the experiments and results presented in the paper offer substantial evidence to validate the scientific hypotheses related to geographic disparities in text-to-image models. The detailed analysis provided by the Decomposed-DIG evaluation protocol and the positive impact of different prompting strategies on background diversity support the need for more refined evaluations and informed mitigations to ensure accurate and representative image generations across global regions .


What are the contributions of this paper?

The paper makes several key contributions:

  • Introducing a new set of metrics called Decomposed Indicators of Disparities in Image Generation (Decomposed-DIG) to measure geographic disparities in the depiction of objects and backgrounds in generated images .
  • Auditing a widely used latent diffusion model using Decomposed-DIG and finding that generated images depict objects with better realism than backgrounds, with backgrounds in generated images showing larger regional disparities than objects .
  • Pinpointing specific examples of disparities, such as stereotypical background generation in Africa, challenges in generating modern vehicles in Africa, and unrealistic placement of some objects in outdoor settings .
  • Using a new prompting structure enabled by Decomposed-DIG to achieve a 52% improvement in the worst-region and a 20% average improvement in generated background diversity .
  • Identifying that the latent diffusion model contributes to geographic disparities primarily through the depiction of backgrounds, with specific failures like the absence of red sedans for Africa and placing cooking pots outdoors for Europe .
  • Experimenting with a new prompting strategy as an early mitigation attempt, showing that Decomposed-DIG can inform strategies leading to significant improvements in representation diversity of backgrounds for the worst-performing region .

What work can be continued in depth?

Further research can be conducted to delve deeper into the impact of prompting strategies on mitigating geographic disparities in text-to-image models. Specifically, exploring the effectiveness of different prompt templates, such as defining geographic information as adjectives like "European car," in improving background diversity and realism could be a valuable area of study . Additionally, investigating the nuances of how prompting influences the representation diversity of backgrounds for various regions, especially the worst-performing regions, could provide insights into more effective mitigation strategies . This research could contribute to enhancing the accuracy and inclusivity of generative vision models across different global regions.

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