TY - GEN
T1 - Comparing Hybrid NN-HMM and RNN for Temporal Modeling in Gesture Recognition
AU - Granger, Nicolas
AU - El Yacoubi, Mounîm A.
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - This paper provides an extended comparison of two temporal models for gesture recognition, namely Hybrid Neural Network-Hidden Markov Models (NN-HMM) and Recurrent Neural Networks (RNN) which have lately claimed the state-the-art performances. Experiments were conducted on both models in the same body of work, with similar representation learning capacity and comparable computational costs. For both solutions, we have integrated recent contributions to the model architectures and training techniques. We show that, for this task, Hybrid NN-HMM models remain competitive with Recurrent Neural Networks in a standard setting. For both models, we analyze the influence of the training objective function on the final evaluation metric. We further tested the influence of temporal convolution to improve context modeling, a technique which was recently reported to improve the accuracy of gesture recognition.
AB - This paper provides an extended comparison of two temporal models for gesture recognition, namely Hybrid Neural Network-Hidden Markov Models (NN-HMM) and Recurrent Neural Networks (RNN) which have lately claimed the state-the-art performances. Experiments were conducted on both models in the same body of work, with similar representation learning capacity and comparable computational costs. For both solutions, we have integrated recent contributions to the model architectures and training techniques. We show that, for this task, Hybrid NN-HMM models remain competitive with Recurrent Neural Networks in a standard setting. For both models, we analyze the influence of the training objective function on the final evaluation metric. We further tested the influence of temporal convolution to improve context modeling, a technique which was recently reported to improve the accuracy of gesture recognition.
KW - End-to-End learning
KW - Gesture recognition
KW - Hybrid NN-HMM
KW - RNN
KW - Representation learning
UR - https://www.scopus.com/pages/publications/85035148453
U2 - 10.1007/978-3-319-70096-0_16
DO - 10.1007/978-3-319-70096-0_16
M3 - Conference contribution
AN - SCOPUS:85035148453
SN - 9783319700953
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 156
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Zhao, Dongbin
A2 - El-Alfy, El-Sayed M.
A2 - Liu, Derong
A2 - Xie, Shengli
A2 - Li, Yuanqing
PB - Springer Verlag
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
Y2 - 14 November 2017 through 18 November 2017
ER -