Search Results for author:
Found 6 papers, 3 papers with code
Would Deep Generative Models Amplify Bias in Future Models?
We investigate the impact of deep generative models on potential social biases in upcoming computer vision models.
Model-Agnostic Gender Debiased Image Captioning
From this observation, we hypothesize that there are two types of gender bias affecting image captioning models: 1) bias that exploits context to predict gender, and 2) bias in the probability of generating certain (often stereotypical) words because of gender.
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations.
Gender and Racial Bias in Visual Question Answering Datasets
Our findings suggest that there are dangers associated to using VQA datasets without considering and dealing with the potentially harmful stereotypes.
Quantifying Societal Bias Amplification in Image Captioning
We study societal bias amplification in image captioning.
A Picture May Be Worth a Hundred Words for Visual Question Answering
This paper delves into the effectiveness of textual representations for image understanding in the specific context of VQA.
