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BURST

Introduced by Ali Athar et al. in BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video25 Sep 2022 archive 2025-07-28

BURST is a benchmark suite built upon TAO that requires tracking and segmenting multiple objects from camera video. The benchmark contains 6 different sub-tasks divided into 2 groups that all share the same data for training/validation/testing.

Class-guided
  1. Common: Track and segment all objects belonging to a set of 78 common classes (based on the COCO class set)
  2. Long-tail: Track and segment all objects belonging to an extended set of 482 object classes (based on the LVIS class set)
  3. Open-world: Methods are only allowed to use the annotations of the 78 common classes during training, but during inference they are expected to track and segment all 482 object classes (class label predictions are not required)
Exemplar-guided
  1. Mask: Track and segment all objects in the video for which the first-frame object masks are given. This task is identical to Video Object Segmentation (VOS).
  2. Box: Track and segment all objects in the video for which the first-frame object bounding-boxes are given.
  3. Point: Track and segment all objects in the video for which we are only given the (x,y) point coordinates of the mask centroid in the first-frame in which the objects appear.

An illustration of the task hierarchy is given here and a detailed explanation is given in Sec. 5 of the dataset paper

Benchmarks archive 2025-07-28

All 5 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

5 shown of 5 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 18. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
General Object Foundation Model for Images and Videos at Scale 1 4 14 Dec 2023 ran 8 of 13 samples (5 unverified)
Putting the Object Back into Video Object Segmentation 1 4 19 Oct 2023 ran 3 of 5 samples (2 unverified)
Tracking Anything with Decoupled Video Segmentation 1 2 7 Sep 2023 ran 7 of 10 samples (3 unverified; 10 pointer-only for licence)
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video 1 2 25 Sep 2022 not harvested
Opening Up Open World Tracking 0 1 1 Jan 2022 not harvested

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

MIT

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • BURST-test
  • BURST-val
  • BURST Point Exemplar-guided (test)
  • BURST Point Exemplar-guided (val)
  • BURST Box Exemplar-guided (test)
  • BURST Box Exemplar-guided (val)
  • BURST Mask Exemplar-guided (test)
  • BURST Mask Exemplar-guided (val)
  • BURST Open-world Class-guided (test)
  • BURST Open-world Class-guided (val)
  • BURST Long-tail Class-guided (test)
  • BURST Long-tail Class-guided (val)
  • BURST Common Class-guided (test)
  • BURST Common Class-guided (val)
  • Exemplar-guided - point - test
  • Exemplar-guided - box - test
  • Exemplar-guided - mask - test
  • Class-guided - open-world - test
  • Class-guided - long-tail - test
  • Class-guided - common - test
  • Exemplar-guided - point - val
  • Exemplar-guided - box - val
  • Exemplar-guided - mask - val
  • Class-guided - open-world - val
  • Class-guided - long-tail - val
  • Class-guided - common - val
  • Class-guided (common)
  • BURST

28 variant names, as the archive lists them.

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