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- W4289285280 abstract "We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset. We find foreground objects in low-level vision data using a John Tukey-inspired measure of outlierness. This Tukey-inspired measure also estimates the reliability of each data source as video characteristics change (e.g., a camera starts moving). The proposed method achieves state-of-the-art results for strictly unsupervised video object segmentation on the challenging DAVIS dataset. Finally, we use a variant of the Tukey-inspired measure to combine the output of multiple segmentation methods, including those using supervision during training, runtime, or both. This collectively more robust method of segmentation improves the Jaccard measure of its constituent methods by as much as 28%." @default.
- W4289285280 created "2022-08-02" @default.
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- W4289285280 date "2018-11-19" @default.
- W4289285280 modified "2023-10-17" @default.
- W4289285280 title "Tukey-Inspired Video Object Segmentation" @default.
- W4289285280 doi "https://doi.org/10.48550/arxiv.1811.07958" @default.
- W4289285280 hasPublicationYear "2018" @default.
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