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 …

Abstract Generalization in Visual Question Answering (VQA) requires models to answer
questions about images with contexts beyond the training distribution. Existing attempts …

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 …

Visual question answering is a multimodal task that requires the joint comprehension of
visual and textual information. However, integrating visual and textual semantics solely …

The goal of Visual Question Answering (VQA) is to test the reasoning ability of an intelligent
agent by evaluating visual and textual information. However, recent studies suggest that …

Recent observations have revealed that Visual Question Answering models are susceptible
to learning the spurious correlations formed by dataset biases, ie, the language priors …

Out-of-Domain (OOD) intent classification is an important task for a dialog system, as it
allows for appropriate responses to be generated. Previous studies aiming to solve the OOD …

Transformer-based architectures have recently demonstrated remarkable performance in
the Visual Question Answering (VQA) task. However, such models are likely to disregard …

Despite the excellent performance of vision-language pre-trained models (VLPs) on
conventional VQA task, they still suffer from two problems: First, VLPs tend to rely on …

Audio-Visual Question Answering (AVQA) is a complex multi-modal reasoning task,
demanding intelligent systems to accurately respond to natural language queries based on …