Pooling Operations
Adaptive Feature Pooling
Introduced by Liu et al. in Path Aggregation Network for Instance SegmentationAdaptive Feature Pooling pools features from all levels for each proposal in object detection and fuses them for the following prediction. For each proposal, we map them to different feature levels. Following the idea of Mask R-CNN, RoIAlign is used to pool feature grids from each level. Then a fusion operation (element-wise max or sum) is utilized to fuse feature grids from different levels.
The motivation for this technique is that in an FPN we assign proposals to different feature levels based on the size of proposals, which could be suboptimal if images with small differences are assigned to different levels, or if the importance of features is not strongly correlated to their level which they belong.
Source: Path Aggregation Network for Instance Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 6 | 11.76% |
| Object Detection | 5 | 9.80% |
| Instance Segmentation | 3 | 5.88% |
| Small Object Detection | 2 | 3.92% |
| Object | 2 | 3.92% |
| Real-Time Object Detection | 2 | 3.92% |
| Traffic Sign Detection | 1 | 1.96% |
| Defect Detection | 1 | 1.96% |
| Insulator Defect Detection | 1 | 1.96% |
Dense Connections
RoIAlign
