Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
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Date
2018
Publication Type
Conference Paper
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yes
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Abstract
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in a learned embedding space: we embed pixels of the same object instance into the vicinity of each other, using a fully convolutional network trained by a modified triplet loss as the embedding model. Then the annotated pixels are set as reference and the rest of the pixels are classified using a nearest-neighbor approach. The proposed method supports different kinds of user input such as segmentation mask in the first frame (semi-supervised scenario), or a sparse set of clicked points (interactive scenario). In the semi-supervised scenario, we achieve results competitive with the state of the art but at a fraction of computation cost (275 milliseconds per frame). In the interactive scenario where the user is able to refine their input iteratively, the proposed method provides instant response to each input, and reaches comparable quality to competing methods with much less interaction.
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published
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2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Pages / Article No.
1189 - 1198
Publisher
IEEE
Event
31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
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03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)