MOSE (coMplex video Object SEgmentation) is a video object segmentation (VOS) dataset introduced to study VOS under complex, realistic scenes where target objects are often small, inconspicuous, heavily occluded, disappear/reappear, or occur in crowded environments. MOSE contains 2,149 video clips with 5,200 target objects and 431,725 high-quality per-frame object segmentation masks (videos are typically 1920×1080 and 5–60 seconds long). The dataset was created to benchmark tracking-and-segmentation robustness in challenging scenarios; standard VOS metrics such as the J&F (region similarity J and contour accuracy F) are used for evaluation. The dataset and benchmark were published in the ICCV 2023 paper "MOSE: A New Dataset for Video Object Segmentation in Complex Scenes" (arXiv:2302.01872) and have an associated project/competition site (MOSE challenge / eval servers).
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