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Deep neural networks facilitate video question answering (VideoQA), but thereal-world applications on video streams such as CCTV and live cast placehigher demands on the solver. To address the challenges of VideoQA on longvideos of unknown length, we define a new set of problems called OnlineOpen-ended Video Question Answering (O^2VQA). It requires an onlinestate-updating mechanism for the solver to decide if the collected informationis sufficient to conclude an answer. We then propose a Confidence-basedEvent-centric Online Video Question Answering (CEO-VQA) model to solve thisproblem. Furthermore, a dataset called Answer Target in Background Stream(ATBS) is constructed to evaluate this newly developed online VideoQAapplication. Compared to the baseline VideoQA method that watches the wholevideo, the experimental results show that the proposed method achieves asignificant performance gain.

Confidence-based Event-centric Online Video Question Answering on a Newly Constructed ATBS Dataset