36 citations · 50 across the 5 of their papers we have counts for
5 papers
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.…
Gender and Racial Bias in Visual Question Answering Datasets
Yusuke Hirota, Yuta Nakashima, Noa Garcia
Vision-and-language tasks have increasingly drawn more attention as a means to evaluate human-like reasoning in machine learning models. A popular task in the field is visual quest…
Quantifying Societal Bias Amplification in Image Captioning
Yusuke Hirota, Yuta Nakashima, Noa Garcia
We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and…
A Picture May Be Worth a Hundred Words for Visual Question Answering
Yusuke Hirota, Noa Garcia, Mayu Otani +4
How far can we go with textual representations for understanding pictures? In image understanding, it is essential to use concise but detailed image representations. Deep visual fe…