InstaBoost

2000년 도입 · 논문 2편에서 사용

InstaBoost 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 $\mathbf{H}$.

출처: InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting

소개 논문: InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting