NEWSKVQA: Knowledge-Aware News Video Question Answering
This addresses a gap in video question answering for news domains, enabling applications like video indexing and retrieval, though it is incremental as it extends existing methods to a new data type.
The paper tackles the problem of answering knowledge-based questions in news videos by curating a new dataset of 12K videos with 1M question-answer pairs and proposing a novel multi-modal approach, achieving a strong baseline performance.
Answering questions in the context of videos can be helpful in video indexing, video retrieval systems, video summarization, learning management systems and surveillance video analysis. Although there exists a large body of work on visual question answering, work on video question answering (1) is limited to domains like movies, TV shows, gameplay, or human activity, and (2) is mostly based on common sense reasoning. In this paper, we explore a new frontier in video question answering: answering knowledge-based questions in the context of news videos. To this end, we curate a new dataset of 12K news videos spanning across 156 hours with 1M multiple-choice question-answer pairs covering 8263 unique entities. We make the dataset publicly available. Using this dataset, we propose a novel approach, NEWSKVQA (Knowledge-Aware News Video Question Answering) which performs multi-modal inferencing over textual multiple-choice questions, videos, their transcripts and knowledge base, and presents a strong baseline.