Proposal Filtering
Adaptive NMS
Introduced by Liu et al. in Adaptive NMS: Refining Pedestrian Detection in a CrowdAdaptive Non-Maximum Suppression is a non-maximum suppression algorithm that applies a dynamic suppression threshold to an instance according to the target density. The motivation is to find an NMS algorithm that works well for pedestrian detection in a crowd. Intuitively, a high NMS threshold keeps more crowded instances while a low NMS threshold wipes out more false positives. The adaptive-NMS thus applies a dynamic suppression strategy, where the threshold rises as instances gather and occlude each other and decays when instances appear separately. To this end, an auxiliary and learnable sub-network is designed to predict the adaptive NMS threshold for each instance.
Source: Adaptive NMS: Refining Pedestrian Detection in a Crowd
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Fine-Grained Image Classification | 1 | 16.67% |
| Fine-Grained Image Recognition | 1 | 16.67% |
| Fine-Grained Visual Categorization | 1 | 16.67% |
| Object Recognition | 1 | 16.67% |
| Object Detection | 1 | 16.67% |
| Pedestrian Detection | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
