Being widely used in learning unbiased visual question answering (VQA) models, Data
Augmentation (DA) helps mitigate language biases by generating extra training samples …

Most existing visual question answering (VQA) models strongly rely on language bias to
answer questions, ie, they always tend to fit question-answer pairs on the train split and …

The performance of visual question answering (VQA) has witnessed great progress over the
last few years. However, many current VQA models tend to rely on superficial linguistic …

Most existing Visual Question Answering (VQA) systems tend to overly rely on the language
bias and hence fail to reason from the visual clue. To address this issue, we propose a novel …

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

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

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 …

Most Visual Question Answering (VQA) models are faced with language bias when learning
to answer a given question, thereby failing to understand multimodal knowledge …

Most Visual Question Answering (VQA) models suffer from the language prior problem,
which is caused by inherent data biases. Specifically, VQA models tend to answer questions …

Despite most state-of-the-art models having achieved amazing performance in visual
question answering (VQA), they usually utilize biases to answer the question. Recently …