Details
Poster
Presenter(s)
Display Name
Yunyi Li
- Affiliation
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AffiliationPeking University Shenzhen Graduate School
- Country
Abstract
In this paper, we propose a CNN based method for the semi-supervised video object segmentation, where a hybrid encoder-decoder network is designed to generate pixel-wise foreground object segmentation in use of both spatial and temporal information. In order to minimize cumulative error of the network as much as possible, we develop a two-stage training scheme: alternate training and back-propagation-through-time training. Then the performances of our method and other state-of-the-art ones are compared on two annotated video segmentation databases. Furthermore, we also run an extensive ablation study to test the effects of different components from our method.