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Gaussian dynamic convolution for efficient single-image segmentation
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Sun, Xin, Chen, Changrui, Wang, Xiaorui, Dong, Junyu, Zhou, Huiyu and Chen, Sheng (2022) Gaussian dynamic convolution for efficient single-image segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 32 (5). pp. 2937-2948. doi:10.1109/tcsvt.2021.3096814 ISSN 1051-8215.
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Official URL: https://doi.org/10.1109/tcsvt.2021.3096814
Abstract
Interactive single-image segmentation is ubiquitous in the scientific and commercial imaging software. Lightweight neural network is one practical and effective way to accomplish the single-image segmentation task. This work focuses on the single-image segmentation problem only with some seeds such as scribbles. Inspired by the dynamic receptive field in the human being’s visual system, we propose the Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the contextual information for neural networks. The core idea is randomly selecting the spatial sampling area according to the Gaussian distribution offsets. Our GDC can be easily used as a module to build lightweight or complex segmentation networks. We adopt the proposed GDC to address the typical single-image segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation. Experiments demonstrate that the GDC outperforms other existing convolutions on three benchmark segmentation datasets including Pascal-Context, Pascal-VOC 2012, and Cityscapes. Additional experiments are also conducted to illustrate that the GDC can produce richer and more vivid features compared with other convolutions. In general, our GDC is conducive to the convolutional neural networks to form an overall impression of the image.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Journal or Publication Title: | IEEE Transactions on Circuits and Systems for Video Technology | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1051-8215 | ||||||||
| Official Date: | 5 May 2022 | ||||||||
| Dates: |
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| Volume: | 32 | ||||||||
| Number: | 5 | ||||||||
| Page Range: | pp. 2937-2948 | ||||||||
| DOI: | 10.1109/tcsvt.2021.3096814 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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