Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

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 …

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 …

Abstract The Visual Question Answering (VQA) task is an important research direction in the
field of artificial intelligence, which requires a model that can simultaneously understand …

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 …

Deep neural networks have been critical in the task of Visual Question Answering (VQA),
with research traditionally focused on improving model accuracy. Recently, however, there …

The Visual Question Answering (VQA) task has recently become notorious because models
are prone to predicting well-educated" guesses" as answers rather than deriving them …

Recent research has revealed the notorious language prior problem in visual question
answering (VQA) tasks based on visual-textual interaction, which indicates that well …