RoI Feature Extractors
Position-Sensitive RoI Pooling
Introduced by Dai et al. in R-FCN: Object Detection via Region-based Fully Convolutional NetworksPosition-Sensitive RoI Pooling layer aggregates the outputs of the last convolutional layer and generates scores for each RoI. Unlike RoI Pooling, PS RoI Pooling conducts selective pooling, and each of the $k$ × $k$ bin aggregates responses from only one score map out of the bank of $k$ × $k$ score maps. With end-to-end training, this RoI layer shepherds the last convolutional layer to learn specialized position-sensitive score maps.
Source: R-FCN: Object Detection via Region-based Fully Convolutional Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 24 | 36.92% |
| General Classification | 3 | 4.62% |
| Object Recognition | 2 | 3.08% |
| Traffic Sign Detection | 2 | 3.08% |
| Semantic Segmentation | 2 | 3.08% |
| Real-Time Object Detection | 2 | 3.08% |
| Image Classification | 2 | 3.08% |
| Classification | 2 | 3.08% |
| Object Tracking | 1 | 1.54% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
