The document presents a method for improving object detection by acquiring localization confidence from predicted intersection-over-union (IOU) between predicted and ground-truth bounding boxes. It addresses two problems: 1) accurate boxes are eliminated in non-maximum suppression due to using classification confidence rather than IOU, and 2) bounding box regression can degrade localization with multiple applications. The proposed method uses an IOU prediction network on transformed regions of interest, a differentiable PrRoI pooling layer, and IOU-guided non-maximum suppression. Experimental results show improved precision and recall over alternative approaches.

1. Acquisition of Localization Confidence
for Accurate Object Detection
Presentation by Haiyan Wang
Media Lab
08/24/2018
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2. Outline
• Introduction
• Two problems
• IOU-Net
• PrRoI Pooling
• IoU-guided NMS
• Bounding box refinement as an optimization procedure
• Experiment result
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3. Introduction
• Object detection
• Distinguish bg/fg
• NMS
(Non-maximum-suppression)
• Bounding box regression
(classification confidence
localization confidence = IOU)
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4. Problem 1
• Accurate localized bounding boxes will be incorrectly eliminated in
the traditional NMS procedure.
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5. • The correlation between the IOU of bounding boxes with the
matched ground-truth and the classification/localization confidence
• The correlation coefficients are: (a) 0.217, (b) 0.617
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6. Problem 2
• Bounding box regression may degenerate the localization of input
bounding boxes If applied for multiple times.
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7. • Non-monotonic bounding box regression
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8. IOU-Net
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9. • Jittered RoIs: manually transform them with a set of randomized
parameters, then remove from this candidate set the bounding boxes
having an IoU less than Ωtrain= 0.5 with the matched ground-truth
• The extracted features are fed into a two-layer network for the IOU
prediction.
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10. PrRoI Pooling
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11. • PrPool(Bin,F) is differentiable w.r.t. the coordinates of bin
• Since we avoids any quantization, PrPool is continuouslydifferentiable
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12. IoU-guided NMS
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• Use the predicted IoU instead of the classification confidence as the
ranking keyword for bounding boxes.
• Confidence Clustering, for a group of bounding boxes matchingthe
same ground-truth, we take the most confident prediction for the
class label

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14. Precision
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15. Recall
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16. Bounding box refinement as an optimization
procedure
• Traditional bounding box refinement
• IoU-Net directly estimates , While the proposed
PreciseRoI Pooling layer enables the computation of the gradient of
IoU w.r.t. bounding box coordinates, we can directly use gradient
ascent method to find the optimal solution to Equation
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18. Experiment result
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