TY - GEN
T1 - Multi-scale regional attention networks for pain estimation
AU - Cui, Shaoxing
AU - Huang, Dong
AU - Ni, Yue
AU - Feng, Xiaoyi
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/5/21
Y1 - 2021/5/21
N2 - Pain is a common treatment response of patients in clinical medicine, which indicates the patient's status. Of late, automatic pain estimation methods have received more and more attention due to their convenience and objectivity. However, the previous researches mostly employ existing models or frameworks, without the exploration of pain locality. In this paper, we propose Multi-Scale Regional Attention Networks (MSRAN), to adaptively capture the importance of facial pain regions. Specifically, the proposed MSRAN aggregates and embeds varied number of multi-scale region features by a convolutional neural network with self-attention module for the locality of pain. Then, the proposed relation-attention module is leveraged to explore the relationship of pain regions. Last, the well-designed loss function is employed to increase the discrimination of model. We validate our MSRAN on the public pain dataset, which shows the effectiveness of the proposed method.
AB - Pain is a common treatment response of patients in clinical medicine, which indicates the patient's status. Of late, automatic pain estimation methods have received more and more attention due to their convenience and objectivity. However, the previous researches mostly employ existing models or frameworks, without the exploration of pain locality. In this paper, we propose Multi-Scale Regional Attention Networks (MSRAN), to adaptively capture the importance of facial pain regions. Specifically, the proposed MSRAN aggregates and embeds varied number of multi-scale region features by a convolutional neural network with self-attention module for the locality of pain. Then, the proposed relation-attention module is leveraged to explore the relationship of pain regions. Last, the well-designed loss function is employed to increase the discrimination of model. We validate our MSRAN on the public pain dataset, which shows the effectiveness of the proposed method.
KW - Attention mechanism
KW - Deep neural networks
KW - Discriminative loss function
KW - Multi-scale method
KW - Pain estimation
UR - http://www.scopus.com/inward/record.url?scp=85121759408&partnerID=8YFLogxK
U2 - 10.1145/3473258.3473259
DO - 10.1145/3473258.3473259
M3 - 会议稿件
AN - SCOPUS:85121759408
T3 - ACM International Conference Proceeding Series
SP - 1
EP - 8
BT - ICBBT 2021 - Proceedings of 2021 13th International Conference on Bioinformatics and Biomedical Technology
PB - Association for Computing Machinery
T2 - 13th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2021
Y2 - 21 May 2021 through 23 May 2021
ER -