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Understanding and conversing about dynamic scenes is one of the keycapabilities of AI agents that navigate the environment and convey usefulinformation to humans. Video question answering is a specific scenario of suchAI-human interaction where an agent generates a natural language response to aquestion regarding the video of a dynamic scene. Incorporating features frommultiple modalities, which often provide supplementary information, is one ofthe challenging aspects of video question answering. Furthermore, a questionoften concerns only a small segment of the video, hence encoding the entirevideo sequence using a recurrent neural network is not computationallyefficient. Our proposed question-guided video representation module efficientlygenerates the token-level video summary guided by each word in the question.The learned representations are then fused with the question to generate theanswer. Through empirical evaluation on the Audio Visual Scene-aware Dialog(AVSD) dataset, our proposed models in single-turn and multi-turn questionanswering achieve state-of-the-art performance on several automatic naturallanguage generation evaluation metrics.

Learning Question-Guided Video Representation for Multi-Turn Video Question Answering