Video Question Answering: Datasets, Algorithms and Challenges
This work organizes existing knowledge to help researchers in computer vision and natural language processing, but it is incremental as it synthesizes prior studies without introducing new methods or data.
The paper addresses the lack of a comprehensive survey in Video Question Answering (VideoQA), providing a taxonomy and analysis of datasets, algorithms, and challenges to advance the field.
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. It has earned increasing attention with recent research trends in joint vision and language understanding. Yet, compared with ImageQA, VideoQA is largely underexplored and progresses slowly. Although different algorithms have continually been proposed and shown success on different VideoQA datasets, we find that there lacks a meaningful survey to categorize them, which seriously impedes its advancements. This paper thus provides a clear taxonomy and comprehensive analyses to VideoQA, focusing on the datasets, algorithms, and unique challenges. We then point out the research trend of studying beyond factoid QA to inference QA towards the cognition of video contents, Finally, we conclude some promising directions for future exploration.