Fast Portrait Segmentation with Highly Light-weight Network
Abstract
In this paper, we describe a fast and light-weight portrait segmentation method based on a new highly light-weight backbone (HLB) architecture. The core element of HLB is a bottleneck-based factorized block (BFB) that has much fewer parameters than existing alternatives while keeping good learning capacity. Consequently, the HLB-based portrait segmentation method can run faster than the existing methods yet retaining the competitive accuracy performance with state-of-the-arts. Experiments conducted on two benchmark datasets demonstrate the effectiveness and efficiency of our method.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2019
- DOI:
- 10.48550/arXiv.1910.08695
- arXiv:
- arXiv:1910.08695
- Bibcode:
- 2019arXiv191008695L
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition