Artificial Intelligence techniques powered by deep neural nets have achieved much success
in several application domains, most significantly and notably in the Computer Vision …

This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Abstract Knowledge-based visual question answering (VQA) involves answering questions
that require external knowledge not present in the image. Existing methods first retrieve …

We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …

Recently, a significant amount of research has been investigated on interpretation of deep
neural networks (DNNs) which are normally processed as black box models. Among the …

Recent research has demonstrated that the multi-task fine-tuning of multi-modal Large
Language Models (LLMs) using an assortment of annotated downstream vision-language …

Many high-level skills that are required for computer vision tasks, such as parsing questions,
comparing and contrasting semantics, and writing descriptions, are also required in other …

Natural language explanation (NLE) models aim at explaining the decision-making process
of a black box system via generating natural language sentences which are human-friendly …

Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated textual
explanations. These explanations are used downstream in three ways: as data …

Recently, there has been an increasing number of efforts to introduce models capable of
generating natural language explanations (NLEs) for their predictions on vision-language …