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Abstract:
Interest point detection in still images is a well-studied topic in computer vision.
In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this paper we approach the problem by emphlearning a detector from examples: we record eye movements of human subjects watching video sequences and train a neural network to predict which locations are likely to become eye movement targets. We show that our detector outperforms current spatiotemporal interest point architectures on a standard classification dataset.
