Zero-Shot-Learning (ZSL), fabric recognition, tactile recognition, deep learning
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.
Tsinghua University Press
Feng Wang, Huaping Liu, Fuchun Sun et al. Fabric Recognition Using Zero-Shot Learning. Tsinghua Science and Technology 2019, 24(06): 645-653.