Methods › Computer Vision › Feature Extractors
Feature Extractors
Feature Extractors for object detection are modules used to construct features that can be used for detecting objects. They address issues such as the need to detect multiple-sized objects in an image (and the need to have representations that are suitable for the different scales).
Methods
All 25 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| FPN Feature Pyramid Network | – | 583 |
| TS Spatio-temporal stability analysis | – | 242 |
| PAFPN | – | 124 |
| Bottom-up Path Augmentation | – | 123 |
| DLA Deep Layer Aggregation | – | 94 |
| TUM Thinned U-shape Module | – | 57 |
| BiFPN | – | 48 |
| NEAT Neural Attention Fields | – | 25 |
| NAS-FPN | – | 11 |
| Context Enhancement Module | – | 8 |
| RFB Receptive Field Block | – | 8 |
| FSAF | – | 7 |
| SFAM Scale-wise Feature Aggregation Module | – | 5 |
| Spatial Attention Module (ThunderNet) | – | 4 |
| TridentNet Block | – | 4 |
| MatrixNet | – | 3 |
| Panoptic FPN | – | 3 |
| Cross-resolution features | – | 2 |
| FFMv1 Feature Fusion Module v1 | – | 2 |
| FFMv2 Feature Fusion Module v2 | – | 2 |
| Low-level backbone | – | 2 |
| MLFPN | – | 2 |
| Streaming Module | – | 2 |
| Feature Intertwiner | – | 1 |
| High-level backbone | – | 1 |