Data Augmentation (DA)—generating extra training samples beyond the original training set—
has been widely-used in today's unbiased VQA models to mitigate language biases. Current …

Visual question answering (VQA) is a challenging task, which has attracted more and more
attention in the field of computer vision and natural language processing. However, the …

This paper comprehensively reviews medical VQA models, structures, and datasets,
focusing on combining vision and language. Over 75 models and their statistical and SWOT …

Models for Visual Question Answering (VQA) often rely on the spurious correlations, ie, the
language priors, that appear in the biased samples of training set, which make them brittle …

Recent studies point out that VQA models tend to rely on the language prior in the training
data to answer the questions, which prevents the VQA model from generalization on the out …

Abstract Existing Visual Question Answering models suffer from the language prior, where
the answers provided by the models overly rely on the correlations between questions and …

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by
dataset biases rather than the intended solution. To evaluate the VQA models' reasoning …

Abstract Visual Question Answering (VQA) aims to answer a textual question based on a
given image. Nevertheless, recent studies have shown that VQA models tend to capture the …

Despite the advances in Visual Question Answering (VQA), many VQA models currently
suffer from language priors (ie generating answers directly from questions without using …

Visual question answering requires a system to provide an accurate natural language
answer given an image and a natural language question. However, it is widely recognized …