IoU-Net is an object detection architecture that introduces localization confidence. IoU-Net learns to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective.
Source: Acquisition of Localization Confidence for Accurate Object DetectionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image-to-Image Translation | 1 | 25.00% |
Rgb-T Tracking | 1 | 25.00% |
General Classification | 1 | 25.00% |
Object Detection | 1 | 25.00% |
Component | Type |
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Dense Connections
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Feedforward Networks | |
FPN
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Feature Extractors | |
Precise RoI Pooling
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RoI Feature Extractors | |
RPN
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Region Proposal |