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 …

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

Although remarkable success has been achieved in the last few years on the Visual
Question Answer (VQA) task, most existing models are heavily driven by the surface …

With the booming fields of computer vision and natural language processing, cross‐modal
intersections such as visual question answering (VQA) have become very popular …

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

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 …

The original intention of visual question answering (VQA) models is to infer the answer
based on the relevant information of the question text in the visual image, but many VQA …

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 …

Superficial linguistic correlations is a critical issue for Visual Question Answering (VQA),
where models can achieve high performance by exploiting the connection between question …

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