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Semi-Supervised Video Object Segmentation datasets
archive 2025-07-28
13 datasets carry the task tag "Semi-Supervised Video Object Segmentation" (the task itself: Semi-Supervised Video Object Segmentation), ordered by the archive's paper count. Page 1 of 1: 13 shown of 13. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
Semi-Supervised Video Object Segmentation datasets 1–13 of 13
DAVIS (Densely Annotated VIdeo Segmentation)
The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p.
734 papers · 10 benchmarks
DAVIS17 is a dataset for video object segmentation.
308 papers · 12 benchmarks
DAVIS16 is a dataset for video object segmentation which consists of 50 videos in total (30 videos for training and 20 for testing).
231 papers · 4 benchmarks
Youtube-VOS is a Video Object Segmentation dataset that contains 4,453 videos - 3,471 for training, 474 for validation, and 508 for testing.
203 papers · 10 benchmarks
Our task is to localize and provide a pixel-level mask of an object on all video frames given a language referring expression obtained either by looking at the first frame only or the full video.
82 papers · 1 benchmark
MOSE (Complex Video Object Segmentation)
CoMplex video Object SEgmentation (MOSE) is a dataset to study the tracking and segmenting objects in complex environments.
49 papers · 2 benchmarks
The Visual Object Tracking (VOT) dataset is a collection of video sequences used for evaluating and benchmarking visual object tracking algorithms.
36 papers · 0 benchmarks
BURST is a benchmark suite built upon TAO that requires tracking and segmenting multiple objects from camera video.
18 papers · 5 benchmarks
BL30K is a synthetic dataset rendered using Blender with ShapeNet's data.
11 papers · 0 benchmarks
VOT2020 is a Visual Object Tracking benchmark for short-term tracking in RGB.
9 papers · 1 benchmark
We randomly selected three videos from the Internet, that are longer than 1.5K frames and have their main objects continuously appearing.
6 papers · 1 benchmark
We randomly selected three videos from the Internet, that are longer than 1.5K frames and have their main objects continuously appearing.
2 papers · 1 benchmark
PUMaVOS (Partial and Unusual Masks for Video Object Segmentation)
PUMaVOS is a dataset of challenging and practical use cases inspired by the movie production industry.
2 papers · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.