More than one hundred benchmarks have been developed to test the commonsense
knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems …

Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Explainable visual question answering (VQA) models have been developed with neural
modules and query-based knowledge incorporation to answer knowledge-requiring …

Abstract Knowledge-based visual question answering (VQA) is a vision-language task that
requires an agent to correctly answer image-related questions using knowledge that is not …

Multimodal learning has been a field of increasing interest, aiming to combine various
modalities in a single joint representation. Especially in the area of visiolinguistic (VL) …

Abstract Knowledge-based visual reasoning requires the ability to associate outside
knowledge that is not present in a given image for cross-modal visual understanding. Two …

In recent years, cross-modal reasoning (CMR), the process of understanding and reasoning
across different modalities, has emerged as a pivotal area with applications spanning from …

Visual reasoning refers to the process of solving questions about visual information. At
present, most visual reasoning models are mainly based on deep learning and end-to-end …

Methods for teaching machines to answer visual questions have made significant progress
in recent years, but current methods still lack important human capabilities, including …

Abstract Most existing Visual Question Answering (VQA) systems are constrained to support
domain-specific questions, ie, to train different models separately for different VQA tasks …