Abstract
Yu et al.[1] showed that dilated convolutions are very effective in dense prediction problems such as semantic segmentation. In this work, we propose a new ResNet[2] based convolutional neural network model using dilated convolutions and show that this model can achieve lower error rate for image classification than ResNet with reduction of the number of the parameters of the network by 94% and that this model has high ability to localize objects despite being trained on image-level labels. We evaluated this model on ImageNet[5] which has 50 class labels randomly selected from 1000 class labels.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 452-455 |
| Number of pages | 4 |
| ISBN (Electronic) | 9784901122160 |
| DOIs | |
| Publication status | Published - 2017 Jul 19 |
| Event | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Duration: 2017 May 8 → 2017 May 12 |
Other
| Other | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 17/5/8 → 17/5/12 |
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
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Cite this
Kudo, Y., & Aoki, Y. (2017). Dilated convolutions for image classification and object localization. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 (pp. 452-455). [7986898] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2017.7986898