Fabric recognition using zero-shot learning

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Abstract

In this work, we use a deep learning method to tackle the Zero-Shot Learning (ZSL) problem in tactile material recognition by incorporating the advanced semantic information into a training model. Our main technical contribution is our proposal of an end-to-end deep learning framework for solving the tactile ZSL problem. In this framework, we use a Convolutional Neural Network (CNN) to extract the spatial features and Long Short-Term Memory (LSTM) to extract the temporal features in dynamic tactile sequences, and develop a loss function suitable for the ZSL setting. We present the results of experimental evaluations on publicly available datasets, which show the effectiveness of the proposed method.

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Wang, F., Liu, H., Sun, F., & Pan, H. (2019). Fabric recognition using zero-shot learning. Tsinghua Science and Technology, 24(6), 645–653. https://doi.org/10.26599/TST.2018.9010095

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