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Pr057 mask rcnn

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Taeoh Kim

Tensorflow Korea 논문읽기 모임 PR12의 57번째 발표는 Instance Segmentation Framework인 Mask R-CNN 입니다

Engineering
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Yonsei University MVP Lab.
Bbox Regression Classification RoI from Selective Search RoI Pooling FixedSizeRepresentation
Bbox Regression Classification RoI Pooling FixedSizeRepresentation Bbox Regression Objectness RPN Region Proposal Network
32x32x3 Conv1 Pool1 16x16x64 Conv2 Pool2 8x8x128 Conv3 Pool3 4x4x256 Conv4 Pool4 2x2x512 Conv5 Pool5 1x1x512 1x1x512 Conv 1x1 Heatmap x32 Upsample Softmax Remove Pooling 1x1 Conv for Heatmap Output
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
Sheep Dog Human Sheep Sheep Sheep Sheep
Sheep Dog Human
Dog Human Sheep Sheep Sheep Sheep Sheep
BBox Classification Segmentation Classification
BBox Classification Segmentation Classification Can Separate Cannot Segment
BBox Classification Segmentation Classification Can Separate Cannot Segment Cannot Separate Can Segment
BBox Classification Segmentation Classification Segmentation in BBox Classification + = Can Separate Cannot Segment Cannot Separate Can Segment
BBox Classification Segmentation Classification Segmentation in BBox Classification + = Can Separate Cannot Segment Cannot Separate Can Segment Faster R-CNN FCN
BBox Classification Segmentation Classification Segmentation in BBox Classification Faster R-CNN FCN FCN on BBOX ! + = + = Can Separate Cannot Segment Cannot Separate Can Segment
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
FCN • Pixel-level Classification • Per Pixel Softmax (Multinomial) • Multi Instance
FCN • Pixel-level Classification • Per Pixel Softmax (Multinomial) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
FCN • Pixel-level Classification • Per Pixel Softmax (Multinomial) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
FCN • Pixel-level Classification • Per Pixel Softmax  Sigmoid (Binary) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
FCN • Pixel-level Classification • Per Pixel Softmax  Sigmoid (Binary) • Multi Instance Faster R-CNN • Classification • Instance Level RoI
DB BBox + Class + Mask 𝐿 = 𝐿𝑐𝑙𝑠 + 𝐿 𝑏𝑜𝑥 + 𝐿 𝑚𝑎𝑠𝑘 𝐿𝑐𝑙𝑠: Softmax Cross Entropy 𝐿 𝑏𝑜𝑥: Regression 𝐿 𝑚𝑎𝑠𝑘: Binary Cross Entropy
Training Phase 𝐿 𝑚𝑎𝑠𝑘 = 𝐿𝑐1 + 𝐿𝑐2 + ⋯+ 𝐿𝑐𝑘 𝐿 𝑚𝑎𝑠𝑘 = 𝐿𝑐3 if) GT Class is 3
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
Test Phase Predicts Human Mask Predicts Car Mask Predicts Horse Mask Predicts ...
Test Phase Predicts Human Mask Predicts Car Mask Predicts Horse Mask Predicts ... Winner Takes All
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
SlidefromMaskR-CNNTutorial, K.He.ICCV2017 FasterR-CNN,S.Ren,NIPS2015
SlidefromMaskR-CNNTutorial, K.He.ICCV2017 Deconv 2x2 str2 Deconv 2x2 str2
SlidefromMaskR-CNNTutorial, K.He.ICCV2017 3x3 Conv 4 Layer
SlidefromMaskR-CNNTutorial, K.He.ICCV2017 1x1 Conv 1x1 Conv
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
Bbox Regression Classification RoI Pooling FixedSizeRepresentation Pooled Feature 7x7
RoI Pooling (Fast R-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature RoI Align (Mask R-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature
RoI Pooling (Fast R-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature RoI Align (Mask R-CNN) • Input: Each RoI • Output: 7x7 Pooled Feature
Feature Map RoI Note: Region Proposal Network RoI Prediction = Floating Point Representation
Feature Map RoI
Feature Map RoI
Feature Map RoI Max Pooling
Feature Map RoI Max Pooling
Feature Map RoI
Feature Map RoI
Feature Map RoI 2x2 Subcells for Precision
= 0.15 + 0.25 + 0.25 + 0.35 RoI
Feature Map RoI 2x2 Subcell Max Pooling
Bbox Regression Classification RoI Align Bbox Regression Objectness RPN Binary Mask
Bbox Regression Classification RoI Align Bbox Regression Objectness RPN Binary Mask Paste Back
SlidefromMaskR-CNNTutorial, K.He.ICCV2017
• 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
• 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
Faster R-CNN + Binary Mask Prediction + FCN + RoIAlign
Faster R-CNN + Binary Mask Prediction + FCN + RoIAlign
Detection Performance Improvement
Q&A?

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