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Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance and identifying potential biases.
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How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?. In EMNLP (Short)
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Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
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Towards Measuring the Representation of Subjective Global Opinions in Language Models
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Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
Laura Gustafson, Megan Richards, Melissa Hall, Caner Hazirbas, Diane Bouchacourt, and Mark Ibrahim. 2023 · 2023
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DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
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SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Akshita Jha, Aida Davani, Chandan K. Reddy, Shachi Dave, Vinodkumar Prabhakaran, and Sunipa Dev. 2023 · 2023
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The Role of ImageNet Classes in Fréchet Inception Distance
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Hate Speech Classifiers are Culturally Insensitive. In Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) , Sunipa Dev, Vinodkumar Prabhakaran, David Adelani, Dirk Hovy, and Luciana Benotti (Eds.). Association for Computational Linguistics, Dubrovnik, Croatia, 35–46
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DINOv2: Learning Robust Visual Features without Supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. 2023 · 2023
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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
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AI’s Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (<conf-loc>, <city>Chicago</city>, <state>IL</state>, <country>USA</country>, </conf-loc>) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 506–517
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Beyond web-scraping: Crowd-sourcing a geodiverse dataset. In arXiv preprint
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Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?
Megan Richards, Polina Kirichenko, Diane Bouchacourt, and Mark Ibrahim. 2023 · 2023
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
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