Proposal Filtering
FeatureNMS
Introduced by Salscheider in FeatureNMS: Non-Maximum Suppression by Learning Feature EmbeddingsFeature Non-Maximum Suppression, or FeatureNMS, is a post-processing step for object detection models that removes duplicates where there are multiple detections outputted per object. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance.
Source: FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings
Papers
| Paper | Code | Results | Date | Stars |
|---|
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
