Fast interactive video segmentation to replace tedious pixel labeling of video
Fast interactive video segmentation to replace tedious pixel labeling of video
Fast Interactive Video Object Segmentation with Graph Neural Networks
DeepAI https://deepai.org/publication/fast-interactive-video-object-segmentation-with-graph-neural-networks
arXiv paper abstract https://arxiv.org/abs/2103.03821
arXiv PDF paper https://arxiv.org/pdf/2103.03821v1.pdf
Pixelwise annotation of image sequences can be very tedious for humans. Interactive video object segmentation aims to utilize automatic methods to speed up the process and reduce the workload of the annotators.
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In this paper we present a graph neural network based approach for tackling the problem of interactive video object segmentation. Our network operates on superpixel-graphs which allow us to reduce the dimensionality of the problem by several magnitudes. We show, that our network possessing only a few thousand parameters is able to achieve state-of-the-art performance, while inference remains fast and can be trained quickly with very little data.
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