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Detecting object affordances with Convolutional Neural Networks
2016
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the
doi:10.1109/iros.2016.7759429
dblp:conf/iros/NguyenKCT16a
fatcat:jvhnlje7rjbidbacihywjtcg3u