In visual question answering (VQA), recent advances have well advocated the use of
attention mechanism to precisely link the question to the potential answer areas. As the …

Visual Question Answering (VQA) requires AI models to comprehend data in two domains,
vision and text. Current state-of-the-art models use learned attention mechanisms to extract …

Abstract Visual Question Answering (VQA) is a challenge problem that can advance AI by
integrating several important sub-disciplines including natural language understanding and …

Abstract Despite advances in Visual Question Answering (VQA), the ability of models to
assess their own correctness remains underexplored. Recent work has shown that VQA …

Abstract Visual Question Answering (VQA) is a hot topic task to answer natural language
questions related to the content of visual images. In most VQA models, visual appearance …

Abstract Visual Question Answering (VQA) requires AI models to comprehend data in two
domains, vision and text. Current state-of-the-art models use learned attention mechanisms …

Recent years have seen a lot of efforts in attacking the issue of language priors in the field of
Visual Question Answering (VQA). Among the extensive efforts, causal inference is regarded …

Visual question answering (VQA) is a multi-modal task involving natural language
processing (NLP) and computer vision (CV), which requires models to understand of both …

Visual question answering (VQA) demands simultaneous comprehension of both the image
visual content and natural language questions. In some cases, the reasoning needs the help …

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 …