DAVIS (Densely Annotated VIdeo Segmentation) is a high-quality video object segmentation benchmark providing per-frame, pixel-accurate ground-truth masks for video sequences. The original DAVIS release (Perazzi et al., CVPR 2016) contains 50 high-resolution (Full HD) video sequences with dense annotations intended to benchmark video object segmentation algorithms. The DAVIS challenge was extended in 2017 (Pont-Tuset et al.) to DAVIS 2017, increasing the dataset size and introducing multi-object sequences and a public challenge/benchmark (DAVIS17: ~150 videos, commonly split into train/val/test sets). Common evaluation metrics for DAVIS are region similarity (J) and contour accuracy (F) and the combined J&F measure (mean of J and F). DAVIS is widely used for semi-supervised video object segmentation (first-frame annotation propagation) as well as for unsupervised and tracking-related tasks.
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