ITI-Gen: Inclusive Text-to-Image Generation
Author:
Affiliation:
1. Carnegie Mellon University
2. Google
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376906.pdf?arnumber=10376906
Reference83 articles.
1. Evaluating CLIP: towards characterization of broader capabilities and downstream implications;Agarwal,2021
2. Ethical considerations for collecting human-centric image datasets;Andrews,2023
3. How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?
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1. Auditing and instructing text-to-image generation models on fairness;AI and Ethics;2024-08-01
2. Addressing Bias in Text-to-Image Generation: A Review of Mitigation Methods;2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN);2024-07-18
3. FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems;Information Fusion;2024-07
4. Towards Text Contextual Understanding: Text Feature Fusion GAN for Text-to-Image Generation;2024-06-24
5. In-Between Visuals and Visible: The Impacts of Text-to-Image Generative AI Tools on Digital Image-making Practices in the Global South;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11
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