TY - GEN
T1 - MobileACNet
T2 - 37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022
AU - Jiang, Tao
AU - Zong, Ming
AU - Ma, Yujun
AU - Hou, Feng
AU - Wang, Ruili
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Lightweight CNN models aim to extend the application of deep learning from conventional image classification to mobile edge device-based image classification. However, the accuracy of lightweight CNN models currently is not as comparable as traditional large CNN models. To improve the accuracy of mobile platform-based image classification, we propose MobileACNet, a novel ACNet-based lightweight model based on MobileNetV3 (a popular lightweight CNN for image classification on mobile platforms). Our model adopts a similar idea to ACNet: consider global inference and local inference adaptively to improve the classification accuracy. We improve the MobileNetV3 by replacing the inverted residual block with our proposed adaptive inverted residual module (AIR). Experimental results show that our proposed MobileACNet can effectively improve the image classification accuracy by providing additional adaptive global inference on three public datasets, i.e., Cifar-100 dataset, Tiny ImageNet dataset, and a large-scale dataset ImageNet, for mobile-platform-based image classification.
AB - Lightweight CNN models aim to extend the application of deep learning from conventional image classification to mobile edge device-based image classification. However, the accuracy of lightweight CNN models currently is not as comparable as traditional large CNN models. To improve the accuracy of mobile platform-based image classification, we propose MobileACNet, a novel ACNet-based lightweight model based on MobileNetV3 (a popular lightweight CNN for image classification on mobile platforms). Our model adopts a similar idea to ACNet: consider global inference and local inference adaptively to improve the classification accuracy. We improve the MobileNetV3 by replacing the inverted residual block with our proposed adaptive inverted residual module (AIR). Experimental results show that our proposed MobileACNet can effectively improve the image classification accuracy by providing additional adaptive global inference on three public datasets, i.e., Cifar-100 dataset, Tiny ImageNet dataset, and a large-scale dataset ImageNet, for mobile-platform-based image classification.
KW - Adaptive global inference
KW - Lightweight CNN models
KW - Mobile-platform-based image classification
KW - MobileACNet
UR - http://www.scopus.com/inward/record.url?scp=85148027905&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25825-1_26
DO - 10.1007/978-3-031-25825-1_26
M3 - Conference contribution
AN - SCOPUS:85148027905
SN - 9783031258244
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 361
EP - 372
BT - Image and Vision Computing - 37th International Conference, IVCNZ 2022, Revised Selected Papers
A2 - Yan, Wei Qi
A2 - Nguyen, Minh
A2 - Stommel, Martin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 November 2022 through 25 November 2022
ER -