Detection Assignment Rules
Prediction-aware One-To-One
Introduced by Wang et al. in End-to-End Object Detection with Fully Convolutional NetworkPrediction-aware One-To-One, or POTO, is an assignment rule for object detection which dynamically assigns the foreground samples according to the quality of classification and regression simultaneously.
Source: End-to-End Object Detection with Fully Convolutional Network
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
