Proposal Filtering
Non Maximum Suppression
Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\text{IoU} \geq 0.5$ with the box output in the previous step.
Image Credit: Martin Kersner
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 196 | 25.93% |
| Object | 105 | 13.89% |
| Semantic Segmentation | 24 | 3.17% |
| Real-Time Object Detection | 20 | 2.65% |
| Instance Segmentation | 19 | 2.51% |
| Pedestrian Detection | 15 | 1.98% |
| Image Classification | 13 | 1.72% |
| General Classification | 11 | 1.46% |
| Classification | 10 | 1.32% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
