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A Benchmark Dataset and Evaluation Methodology for Video ...

A Benchmark Dataset and Evaluation Methodology forVideo Object SegmentationF. Perazzi1,2J. Pont-Tuset1B. McWilliams2L. Van Gool1M. Gross1,2A. Sorkine-Hornung21 ETH Zurich2 Disney ResearchAbstractOver the years, datasets and benchmarks have proventheir fundamental importance in computer vision research,enabling targeted progress and objective comparisons inmany fields. At the same time, legacy datasets may impendthe evolution of a field due to saturated algorithm perfor-mance and the lack of contemporary, high quality data. Inthis work we present a new Benchmark Dataset and evalu-ation Methodology for the area ofvideo object segmenta-tion.

cal applications, e.g. for processing large datasets, or video post-production and editing in the visual effects industry. What is most striking is the performance gap among state-of-the-art video object segmentation algorithms and closely related methods focusing on image segmentation Figure 1: Sample sequences from our dataset, with ground

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