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Early Action Prediction with Generative Adversarial Networks
2019
IEEE Access
Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic accident before it happens and detecting malicious actions in the monitoring system. In this work, we address this problem by developing an end-to-end architecture that improves the discriminability of features of partially observed videos by assimilating them to features from complete videos. For this purpose, the
doi:10.1109/access.2019.2904857
fatcat:oewrqk7qejbhvjgjyabmlbewje