56 citations · 265 across the 31 of their papers we have counts for
6 papers · 1 filter
Stable Diffusion Exposed: Gender Bias from Prompt to Image
Yankun Wu, Yuta Nakashima, Noa Garcia
Several studies have raised awareness about social biases in image generative models, demonstrating their predisposition towards stereotypes and imbalances. This paper contributes…
Situating the social issues of image generation models in the model life cycle: a sociotechnical approach
Amelia Katirai, Noa Garcia, Kazuki Ide +2
The race to develop image generation models is intensifying, with a rapid increase in the number of text-to-image models available. This is coupled with growing public awareness of…
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
Tong Xiang, Liangzhi Li, Wangyue Li +4
The recent advances in natural language processing (NLP), have led to a new trend of applying large language models (LLMs) to real-world scenarios. While the latest LLMs are astoni…
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis
Yankun Wu, Yuta Nakashima, Noa Garcia
The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of…
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
Noa Garcia, Yusuke Hirota, Yankun Wu +1
The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations. It is known that even small but…
Model-Agnostic Gender Debiased Image Captioning
Yusuke Hirota, Yuta Nakashima, Noa Garcia
Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate such gender bias in image captioning models.…