Methods › Computer Vision › Image Data Augmentation › InstaBoost
InstaBoost
Introduced by Hao-Shu Fang et al. in InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
InstaBoost is a data augmentation technique for instance segmentation that utilises existing instance mask annotations.
Intuitively in a small neighbor area of (x₀, y₀, 1, 0), the probability map P(x, y, s, r) should be high-valued since images are usually continuous and redundant in pixel level. Based on this, InstaBoost is a form of augmentation where we apply object jittering that randomly samples transformation tuples from the neighboring space of identity transform (x₀, y₀, 1, 0) and paste the cropped object following affine transform 𝐇.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Instruction Following by Boosting Attention of Large Language Models 16 Jun 2025 · 0 repositories · arXiv:2506.13734
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InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting 21 Aug 2019 · 3 repositories · arXiv:1908.07801
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Data Augmentation | 1 |
| Instance Segmentation | 1 |
| Instruction Following | 1 |
| Object Detection | 1 |
| Prompt Engineering | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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