"Fabric Recognition Using Zero-Shot Learning" by Feng Wang, Huaping Liu et al.

Home > TSINGHUA-SCIENCE-AND-TECHNOLOGY > Vol. 24 (2019) > Iss. 6

Article Title

Fabric Recognition Using Zero-Shot Learning

Authors

Feng Wang, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Huaping Liu, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Fuchun Sun, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Haihong Pan, College of Mechanical Engineering, Guangxi University, Nanning 530003, China.

Keywords

Zero-Shot-Learning (ZSL), fabric recognition, tactile recognition, deep learning

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.