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Predicting the Category and Attributes of Visual Search Targets Using Deep Gaze Pooling
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Predicting the target of visual search from human gaze data is a challenging problem. In contrast to previous work that focused on predicting specific instances of search targets, we propose the first approach to predict a target's category and attributes. However, state-of-the-art models for categorical recognition require large amounts of training data, which is prohibitive for gaze data. We thus propose a novel Gaze Pooling Layer that integrates gaze information and CNN-based features by an
doi:10.1109/iccvw.2017.322
dblp:conf/iccvw/SattarBF17
fatcat:7opkss22bnhxnmwp7cjamwinb4