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Pr057 mask rcnn
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- 5. 32x32x3 Conv1 Pool1 16x16x64 Conv2 Pool2 8x8x128 Conv3 Pool3 4x4x256 Conv4 Pool4 2x2x512 Conv5 Pool5 1x1x512 1x1x512 Conv 1x1 Heatmap x32Upsample Softmax Remove Pooling 1x1 Conv for Heatmap Output
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- 15. BBox Classification Segmentation Classification Segmentation in BBox Classification + = CanSeparate Cannot Segment Cannot Separate Can Segment
- 16. BBox Classification Segmentation Classification Segmentation in BBox Classification + = CanSeparate Cannot Segment Cannot Separate Can Segment Faster R-CNN FCN
- 17. BBox Classification Segmentation Classification Segmentation in BBox Classification Faster R-CNNFCN FCN on BBOX ! + = + = Can Separate Cannot Segment Cannot Separate Can Segment
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- 28. FCN • Pixel-level Classification •Per Pixel Softmax (Multinomial) • Multi Instance
- 29. FCN • Pixel-level Classification •Per Pixel Softmax (Multinomial) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
- 30. FCN • Pixel-level Classification •Per Pixel Softmax (Multinomial) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
- 31. FCN • Pixel-level Classification •Per Pixel Softmax Sigmoid (Binary) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
- 32. FCN • Pixel-level Classification •Per Pixel Softmax Sigmoid (Binary) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
- 33. DB BBox + Class+ Mask 𝐿 = 𝐿𝑐𝑙𝑠 + 𝐿 𝑏𝑜𝑥 + 𝐿 𝑚𝑎𝑠𝑘 𝐿𝑐𝑙𝑠: Softmax Cross Entropy 𝐿 𝑏𝑜𝑥: Regression 𝐿 𝑚𝑎𝑠𝑘: Binary Cross Entropy
- 34. Training Phase 𝐿 𝑚𝑎𝑠𝑘= 𝐿𝑐1 + 𝐿𝑐2 + ⋯+ 𝐿𝑐𝑘 𝐿 𝑚𝑎𝑠𝑘 = 𝐿𝑐3 if) GT Class is 3
- 35. Training Phase 𝐿 𝑚𝑎𝑠𝑘= 𝐿𝑐1 + 𝐿𝑐2 + ⋯+ 𝐿𝑐𝑘 𝐿 𝑚𝑎𝑠𝑘 = 𝐿𝑐3 if) GT Class is 3 Mask Branch Only Learns How to Mask independent of Class
- 36. Test Phase Predicts HumanMask Predicts Car Mask Predicts Horse Mask Predicts ...
- 37. Test Phase Predicts HumanMask Predicts Car Mask Predicts Horse Mask Predicts ... Winner Takes All
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- 43. SlidefromMaskR-CNNTutorial, K.He.ICCV2017 3x3Conv 4 Layer
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- 48. RoI Pooling (FastR-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature RoI Align (Mask R-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature
- 49. RoI Pooling (FastR-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature RoI Align (Mask R-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature
- 50. Feature Map RoI Note: Region ProposalNetwork RoI Prediction = Floating Point Representation
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- 57. Feature Map RoI 2x2 Subcellsfor Precision
- 58. = 0.15 +0.25 + 0.25 + 0.35 RoI
- 59. Feature Map RoI 2x2 SubcellMax Pooling
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- 64. • Faster R-CNN+ ResNet Deep ResidualLearning for Image Recognition, K He, 2016 CVPR • Faster R-CNN + FPN Feature Pyramid Networks for Object Detection, T.Y.Lin 2017 CVPR
- 65. • Faster R-CNN+ ResNet Deep ResidualLearning for Image Recognition, K He, 2016 CVPR
- 66. • Faster R-CNN+ FPN Feature Pyramid Networks for Object Detection, T.Y.Lin 2017 CVPR
- 68. Faster R-CNN +Binary Mask Prediction + FCN + RoIAlign
- 69. Faster R-CNN +Binary Mask Prediction + FCN + RoIAlign
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