Visual question answering (VQA) is a challenging task, which has attracted more and more
attention in the field of computer vision and natural language processing. However, the …

Visual question answering (VQA) is a prevalent task in real-world, and plays an essential
role in helping the blind understand the physical world. However, due to the real-world …

With the rapid development of deep learning models, great improvements have been
achieved in the Visual Question Answering (VQA) field. However, modern VQA models are …

This paper comprehensively reviews medical VQA models, structures, and datasets,
focusing on combining vision and language. Over 75 models and their statistical and SWOT …

Scene Graph Generation (SGG) is to abstract the objects and their semantic relationships
within a given image. Current SGG performance is mainly limited by the biased predicate …

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 …

With the emergence of large-scale multi-modal foundation models, significant improvements
have been made towards Visual Question Answering (VQA) in recent years via the “Pre …

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

If we compare how humans reason and how deep models reason, humans reason in a
symbolic manner with a formal language called logic, while most deep models reason in …

Visual question answering (VQA) has been intensively studied as a multimodal task,
requiring efforts to bridge vision and language for correct answer inference. Recent attempts …