Image Data Augmentation
InstaBoost
Introduced by Fang et al. in InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-PastingInstaBoost is a data augmentation technique for instance segmentation that utilises existing instance mask annotations.
Intuitively in a small neighbor area of $(x_0, y_0, 1, 0)$, 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 $(x_0, y_0, 1, 0)$ and paste the cropped object following affine transform $\mathbf{H}$.
Source: InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Instance Segmentation | 1 | 33.33% |
| Object Detection | 1 | 33.33% |
| Semantic Segmentation | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
