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Open AccessArticle

Symmetry Encoder-Decoder Network with Attention Mechanism for Fast Video Object Segmentation

1
College of Information and Engineering, Sichuan Agricultural University, Yaan 625014, China
2
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
3
College of Computer Science, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(8), 1006; https://doi.org/10.3390/sym11081006
Received: 22 July 2019 / Revised: 30 July 2019 / Accepted: 1 August 2019 / Published: 4 August 2019
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
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Abstract

Semi-supervised video object segmentation (VOS) has obtained significant progress in recent years. The general purpose of VOS methods is to segment objects in video sequences provided with a single annotation in the first frame. However, many of the recent successful methods heavily fine-tune the object mask in the first frame, which decreases their efficiency. In this work, to address this issue, we propose a symmetry encoder-decoder network with the attention mechanism for video object segmentation (SAVOS) requiring only one forward pass to segment the target object in a video. Specifically, the encoder generates a low-resolution mask with smoothed boundaries, while the decoder further refines the details of the segmentation mask and integrates lower level features progressively. Besides, to obtain accurate segmentation results, we sequentially apply the attention module on multi-scale feature maps for refinement. We conduct several experiments on three challenging datasets (i.e., DAVIS 2016, DAVIS 2017, and SegTrack v2) to show that SAVOS achieves competitive performance against the state-of-the-art. View Full-Text
Keywords: video object segmentation; convolutional neural network; attention mechanism; semi-supervised; encoder-decoder video object segmentation; convolutional neural network; attention mechanism; semi-supervised; encoder-decoder
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Guo, M.; Zhang, D.; Sun, J.; Wu, Y. Symmetry Encoder-Decoder Network with Attention Mechanism for Fast Video Object Segmentation. Symmetry 2019, 11, 1006.

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