Video Question-Answering Techniques, Benchmark Datasets and Evaluation Metrics Leveraging Video Captioning: A Comprehensive Survey

While describing visual data is a trivial task for humans, it an intricate computer. This even more challenging if the video. Comprehending video and called Video Captioning. involves understanding semantics of then generating human-like descriptions It requires collaboration both research communities computer vision natural language processing. The captions generated by captioning can be further utilized retrieval, summarization, question-answering, etc. Question-Answering (video-QA) querying system to obtain answer in response. paper presents brief survey techniques comprehensive review existing techniques, datasets, evaluation metrics video-QA. Video-QA rely on attention mechanism generate relevant results. presented shows that recent works Memory Networks, Generative Adversarial Reinforced Decoders, have capability handle complexities challenges Additionally, graph-based methods, although less explored, give very promising In this article, we discussed emerging directions various application areas