Inspecting the Geographical Representativeness of Images from Text-to-Image Models
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[1] William Yang Wang,et al. Multilingual Conceptual Coverage in Text-to-Image Models , 2023, ACL.
[2] James Y. Zou,et al. Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale , 2022, FAccT.
[3] Kai-Wei Chang,et al. How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? , 2022, EMNLP.
[4] S. Clémençon,et al. Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model , 2022, ICML.
[5] Jason Baldridge,et al. Underspecification in Scene Description-to-Depiction Tasks , 2022, AACL.
[6] Yuanzhen Li,et al. DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Jing Yu Koh,et al. Scaling Autoregressive Models for Content-Rich Text-to-Image Generation , 2022, Trans. Mach. Learn. Res..
[8] David J. Fleet,et al. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding , 2022, NeurIPS.
[9] Aylin Caliskan,et al. Markedness in Visual Semantic AI , 2022, FAccT.
[10] Prafulla Dhariwal,et al. Hierarchical Text-Conditional Image Generation with CLIP Latents , 2022, ArXiv.
[11] B. Ommer,et al. High-Resolution Image Synthesis with Latent Diffusion Models , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Prafulla Dhariwal,et al. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models , 2021, ICML.
[13] Fang Wen,et al. Vector Quantized Diffusion Model for Text-to-Image Synthesis , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Olga Russakovsky,et al. Understanding and Evaluating Racial Biases in Image Captioning , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[15] Kai-Wei Chang,et al. Societal Biases in Language Generation: Progress and Challenges , 2021, ACL.
[16] Ilya Sutskever,et al. Learning Transferable Visual Models From Natural Language Supervision , 2021, ICML.
[17] Alec Radford,et al. Zero-Shot Text-to-Image Generation , 2021, ICML.
[18] Lalana Kagal,et al. Investigating Bias in Image Classification using Model Explanations , 2020, ArXiv.
[19] Sunnie S. Y. Kim,et al. Fair Attribute Classification through Latent Space De-biasing , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Aylin Caliskan,et al. Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases , 2020, FAccT.
[21] Ramya Srinivasan,et al. Biases in Generative Art: A Causal Look from the Lens of Art History , 2020, FAccT.
[22] S. Gelly,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.
[23] Jiasen Lu,et al. X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers , 2020, EMNLP.
[24] N. Sebe,et al. DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis , 2020, ArXiv.
[25] Yi Zhang,et al. Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing , 2020, ACM Multimedia.
[26] Pieter Abbeel,et al. Denoising Diffusion Probabilistic Models , 2020, NeurIPS.
[27] Jianfeng Gao,et al. Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks , 2020, ECCV.
[28] Andrei Barbu,et al. Measuring Social Biases in Grounded Vision and Language Embeddings , 2020, NAACL.
[29] Nanyun Peng,et al. The Woman Worked as a Babysitter: On Biases in Language Generation , 2019, EMNLP.
[30] Furu Wei,et al. VL-BERT: Pre-training of Generic Visual-Linguistic Representations , 2019, ICLR.
[31] Stefan Lee,et al. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks , 2019, NeurIPS.
[32] Wei Chen,et al. DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Radu Soricut,et al. Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning , 2018, ACL.
[34] Trevor Darrell,et al. Women also Snowboard: Overcoming Bias in Captioning Models , 2018, ECCV.
[35] Timnit Gebru,et al. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.
[36] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[37] Dimitris N. Metaxas,et al. StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[38] Bernt Schiele,et al. Generative Adversarial Text to Image Synthesis , 2016, ICML.
[39] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Ruslan Salakhutdinov,et al. Generating Images from Captions with Attention , 2015, ICLR.
[41] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[42] Aaron B. Adcock,et al. Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset , 2023, arXiv.org.
[43] Lukas Struppek,et al. The Biased Artist: Exploiting Cultural Biases via Homoglyphs in Text-Guided Image Generation Models , 2022, ArXiv.
[44] Mohit Bansal,et al. DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers , 2022, ArXiv.
[45] Michelle A. Gan,et al. An Image of Society: Gender and Racial Representation and Impact in Image Search Results for Occupations , 2021 .
[46] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .