TL;DR
A comparative analysis of models from different periods is made, showing the development of VQA in terms of structure and performance, raising existing challenges, and suggesting future directions.
In recent decades, artificial intelligence has made significant progress in understanding and interacting with images. One of the important applications of this technology is Visual Question Answering (VQA), a research field that requires computers to understand and answer questions about images in a natural manner. De...
This systematic literature review (SLR) focuses on multi answer VQA systems and the use of object detection, following the PRISMA 2020 guidelines and proposes a taxonomy of multi-answer VQA organized along four dimensions.
Med-VQA systems help alleviate the diagnostic burden on radiologists by providing decision support to clinicians through the interpretation of clinical queries based on radiological images. Traditional models are classification-based and are limited to a restricted response space. This proves insufficient in complex me...
This survey provides a comprehensive review of developments in NQG, spanning traditional neural approaches to the latest paradigms driven by large language models (LLMs) and multimodal large language models (MLLMs).
Evaluating learner responses is vital in assessment, especially for free-text answers, which are most challenging due to their demand for reasoning and expression. These responses reflect the learner’s understanding of the subject matter. However, manual evaluation is time-consuming and also prone to human error, highl...
A comprehensive survey of various benchmark datasets used in medical LLM tasks, focusing on various aspects of medical knowledge such as electronic health records, doctor–patient dialogues, medical question answering, and medical image captioning, is presented.
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