Prioritized Sampling
IoU-Balanced Sampling
Introduced by Pang et al. in Libra R-CNN: Towards Balanced Learning for Object DetectionIoU-Balanced Sampling is hard mining method for object detection. Suppose we need to sample $N$ negative samples from $M$ corresponding candidates. The selected probability for each sample under random sampling is:
$$ p = \frac{N}{M} $$
To raise the selected probability of hard negatives, we evenly split the sampling interval into $K$ bins according to IoU. $N$ demanded negative samples are equally distributed to each bin. Then we select samples from them uniformly. Therefore, we get the selected probability under IoU-balanced sampling:
$$ p_{k} = \frac{N}{K}*\frac{1}{M_{k}}\text{ , } k\in\left[0, K\right)$$
where $M_{k}$ is the number of sampling candidates in the corresponding interval denoted by $k$. $K$ is set to 3 by default in our experiments.
The sampled histogram with IoU-balanced sampling is shown by green color in the Figure to the right. The IoU-balanced sampling can guide the distribution of training samples close to the one of hard negatives.
Source: Libra R-CNN: Towards Balanced Learning for Object Detection
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 3 | 42.86% |
| Weakly Supervised Object Detection | 1 | 14.29% |
| Ensemble Learning | 1 | 14.29% |
| Medical Object Detection | 1 | 14.29% |
| Object Localization | 1 | 14.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
