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Video Object Segmentation datasets
archive 2025-07-28
19 datasets carry the task tag "Video Object Segmentation" (the task itself: Video Object Segmentation), ordered by the archive's paper count. Page 1 of 1: 19 shown of 19. 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
Video Object Segmentation datasets 1–19 of 19
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
FBMS (Freiburg-Berkeley Motion Segmentation)
The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is an extension of the BMS dataset with 33 additional video sequences.
126 papers · 1 benchmark
SegTrack v2 is a video segmentation dataset with full pixel-level annotations on multiple objects at each frame within each video.
107 papers · 5 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
AVSBench is a pixel-level audio-visual segmentation benchmark that provides ground truth labels for sounding objects.
20 papers · 0 benchmarks
FBMS-59 (Freiburg-Berkeley Motion Segmentation)
The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences.
19 papers · 3 benchmarks
LVOS is a dataset for long-term video object segmentation (VOS).
19 papers · 0 benchmarks
BL30K is a synthetic dataset rendered using Blender with ShapeNet's data.
11 papers · 0 benchmarks
VOST consists of more than 700 high-resolution videos, captured in diverse environments, which are 20 seconds long on average and densely labeled with instance masks.
11 papers · 0 benchmarks
ARMBench is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse.
4 papers · 1 benchmark
M³-VOS (M³-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation)
💡 Description A new benchmark, Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation (M³-VOS), to verify the ability of models to understand object phases, which consists of 479 high-resolution videos spanning over 10…
4 papers · 1 benchmark
ODMS (Object Depth via Motion and Segmentation)
ODMS is a dataset for learning Object Depth via Motion and Segmentation.
2 papers · 0 benchmarks
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
VISOR is a dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video.
1 paper · 0 benchmarks
Infinity AI's Spills Basic Dataset is a synthetic, open-source dataset for safety applications.
0 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.