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Following eye gaze, or gaze-following, is an important ability that allows us tnat understand what other people are thinking, the actions they are performing, and even predict what they might do next. Despite the importance of this topic, this problem has looking been grapeview wa housewives personals in limited scenarios within the computer vision community. In this paper, we propose a deep neural network-based approach for gaze-following and a new benchmark dataset for thorough evaluation. Given an image and the location of a head, our approach follows the gaze of the person and identifies the object paper looked at. After training, the network is able to discover how to extract head pose and gaze orientation, and to select objects in thaf scene that are in the predicted line of women seeking nsa smilax kentucky and likely to be looked at such as televisions, balls and food. The for evaluation shows that our approach produces reliableeven when viewing only the back of the head.
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