Cover Image

TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chinchure, Aditya, Shukla, Pushkar, Bhatt, Gaurav, Salij, Kiri, Hosanagar, Kartik, Sigal, Leonid, Turk, Matthew
Format:	Preprint
Published:	2023
Subjects:	Computer Vision and Pattern Recognition Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models
by: Shukla, Pushkar, et al.
Published: (2025)

BiasConnect: Investigating Bias Interactions in Text-to-Image Models
by: Shukla, Pushkar, et al.
Published: (2025)

Spotlight: Identifying and Localizing Video Generation Errors Using VLMs
by: Chinchure, Aditya, et al.
Published: (2025)

SPIKE-RL: Video-LLMs meet Bayesian Surprise
by: Ravi, Sahithya, et al.
Published: (2025)

Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection
by: Bhatt, Gaurav, et al.
Published: (2024)

Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement
by: Shukla, Pushkar, et al.
Published: (2024)

A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models
by: Girrbach, Leander, et al.
Published: (2025)

Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events
by: Chinchure, Aditya, et al.
Published: (2024)

The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects
by: Wan, Yixin, et al.
Published: (2024)

Emotional Images: Assessing Emotions in Images and Potential Biases in Generative Models
by: Mehta, Maneet, et al.
Published: (2024)

Auditing Gender Presentation Differences in Text-to-Image Models
by: Zhang, Yanzhe, et al.
Published: (2023)

Understanding and evaluating computer vision models through the lens of counterfactuals
by: Shukla, Pushkar
Published: (2025)

Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classification
by: Doh, Miriam, et al.
Published: (2026)

Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation
by: Wei, Mengting, et al.
Published: (2026)

Identifying Implicit Social Biases in Vision-Language Models
by: Hamidieh, Kimia, et al.
Published: (2024)

Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis
by: Struppek, Lukas, et al.
Published: (2022)

Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
by: Wan, Yixin, et al.
Published: (2024)

Visual Concept-driven Image Generation with Text-to-Image Diffusion Model
by: Rahman, Tanzila, et al.
Published: (2024)

On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
by: Vice, Jordan, et al.
Published: (2024)

Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models
by: Mandal, Abhishek, et al.
Published: (2024)

Smiling Women Pitching Down: Auditing Representational and Presentational Gender Biases in Image Generative AI
by: Sun, Luhang, et al.
Published: (2023)

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models
by: Hall, Melissa, et al.
Published: (2024)

From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models
by: Bhatia, Mehar, et al.
Published: (2024)

Alignment-Aware Model Adaptation via Feedback-Guided Optimization
by: Bhatt, Gaurav, et al.
Published: (2026)

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models
by: Jin, Di, et al.
Published: (2024)

A Framework for Critical Evaluation of Text-to-Image Models: Integrating Art Historical Analysis, Artistic Exploration, and Critical Prompt Engineering
by: Foka, Amalia
Published: (2024)

Are Models Biased on Text without Gender-related Language?
by: Belém, Catarina G, et al.
Published: (2024)

ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
by: Jha, Akshita, et al.
Published: (2024)

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images
by: Zhang, Bo, et al.
Published: (2026)

Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on Breast Ultrasound Images
by: Medghalchi, Yasamin, et al.
Published: (2024)

Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
by: Shi, Chuancheng, et al.
Published: (2025)

Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
by: Quaye, Jessica, et al.
Published: (2024)

The Iconicity of the Generated Image
by: van Noord, Nanne, et al.
Published: (2025)

Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities
by: Alsudais, Abdulkareem
Published: (2025)

Joint Generative Modeling of Grounded Scene Graphs and Images via Diffusion Models
by: Xu, Bicheng, et al.
Published: (2024)

Towards Reliable Verification of Unauthorized Data Usage in Personalized Text-to-Image Diffusion Models
by: Li, Boheng, et al.
Published: (2024)

Uncovering Regional Defaults from Photorealistic Forests in Text-to-Image Generation with DALL-E 2
by: Liu, Zilong, et al.
Published: (2024)

Learning Under Low Illumination: A Dataset and Algorithm for Traffic Sign Recognition
by: Mishra, Aditya, et al.
Published: (2025)

KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation
by: Davoodi, Farbod, et al.
Published: (2026)

Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems
by: Jaiswal, Siddharth D, et al.
Published: (2024)