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 …

In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn
spurious correlations in the training set and achieve poor performance in out-of-distribution …

Recent studies have pointed out that many well-developed Visual Question Answering
(VQA) systems suffer from bias problem. Despite the remarkable performance gained on In …

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 …

Visual question answering (VQA) is designed to examine the visual-textual reasoning ability
of an intelligent agent. However, recent observations show that many VQA models may only …

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 great progress of Visual Question Answering (VQA), current VQA models heavily
rely on the superficial correlation between the question type and its corresponding frequent …

Visual question answering (VQA) is challenging not only because the model has to handle
multi-modal information, but also because it is just so hard to collect sufficient training …

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 …