2018/07/01のCVPR2018読み会の発表資料です。 論文 - ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes - Learning to Adapt Structured Output Space for Semantic Segmentation

1. ROAD: Reality Oriented Adaptation for Semantic
Segmentation of Urban Scenes
&
Learning to Adapt Structured Output Space for
Semantic Segmentation
(@doiken23)
1
46
CVPR2018 ( )

2. Profile
:
:
:
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2

3. • ROAD: Reality Oriented Adaptation for Semantic Segmentation of
Urban Scenes
– Y. Chen et al.
• Learning to Adapt Structured Output Space for Semantic
Segmentation.
– Y. H. Tsai et al.
※ Y. Chen ”Domain Adaptive Faster R-CNN for Object Detection in the Wild”
CVPR2018 accept
※
3

4. •
)
•
•
4
Domain Adaptive Faster R-CNN for Object Detection in the
Wild [Y. Chen et al. CVPR2018]

5. (Domain Adaptation)
• 2
5DeepVisual Domain Adaptation:A Survey [M.Wang 2018]

6. (Domain Adaptation)
•
– Unsupervised Domain Adaptation (UDA)
•
➡DNN
➡
6

7. •
7
Source domain Source domain
target domain
source domain

8. •
8
Source domain Source domain
target domain
source domain

9. •
9
Source domain Source domain
target domain
source domain

10. ROAD: Reality Oriented Adaptation for Semantic
Segmentation of Urban Scenes
10

11. • Semantic Segmentation
2
•
overfit
•
– ( )
2
• Target Guided Distillation
• Spatial-Aware Adaptation 11
Playing for Data: GroundTruth from Computer Games [Stephan R. Richter et al. ECCV
2016]

12. 12

13. Target Guided Distillation
• CNN filter
overfit
➡
13
ImageNet

14. Target Guided Distillation
• CNN filter
overfit
➡
14
Segmentation model Pertained model

15. Spatial-Aware Adaptation
•
( )
➡ ( )
➡
15

16. Spatial-Aware Adaptation
•
( )
➡ ( )
➡
16
Advertsarial Learning
(ADDA )

17. 17
segmentation
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18. 18
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Ldist<latexit sha1_base64="7flKPitUYg9WwQ2wBhNZv5BLQTY=">AAACd3ichVHLSsNAFD2Nr1pftW4EF4pFcVVuRFBciW5cuNDWVkGlJHHUYF4k06KG/oA/4EIQFETFz3DjD7joJ4hLBRFceJsGREW9ITNnztxz75kZ3bPMQBLVE0pLa1t7R7Iz1dXd09uX7s+UArfiG6JouJbrr+taICzTEUVpSkuse77QbN0Sa/r+QmN/rSr8wHSdVXnoiS1b23XMHdPQJFPldGbT1uSeoVnhUq0cbnPDWjmdpRxFMfITqDHIIo5lN32FTWzDhYEKbAg4kIwtaAj424AKgsfcFkLmfEZmtC9QQ4q1Fc4SnKExu8/jLq82YtbhdaNmEKkN7mLx77NyBGP0QDf0TPd0S4/0/mutMKrR8HLIs97UCq/cdzxYeP1XZfMssfep+tOzxA5mIq8me/cipnEKo6mvHp08F2bzY+E4XdAT+z+nOt3xCZzqi3G5IvKnSPEDqN+v+ycoTeZUyqkrU9m5+fgpkhjCKCb4vqcxh0Uso8h9D3CGK1wn3pRhZVyZaKYqiVgzgC+hqB9TBpHg</latexit><latexit sha1_base64="7flKPitUYg9WwQ2wBhNZv5BLQTY=">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</latexit><latexit sha1_base64="7flKPitUYg9WwQ2wBhNZv5BLQTY=">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</latexit><latexit sha1_base64="7flKPitUYg9WwQ2wBhNZv5BLQTY=">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</latexit>
Lspt<latexit sha1_base64="DuT3JS0aRi/JiO0evgJOWUXMtKA=">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</latexit><latexit sha1_base64="DuT3JS0aRi/JiO0evgJOWUXMtKA=">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</latexit><latexit sha1_base64="DuT3JS0aRi/JiO0evgJOWUXMtKA=">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</latexit><latexit sha1_base64="DuT3JS0aRi/JiO0evgJOWUXMtKA=">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</latexit>
LROAD = Lseg + 1Ldist + 2Lpst<latexit sha1_base64="IOQr5ijj8Ma3vQ/xSGEmuoSNYG4=">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</latexit><latexit sha1_base64="IOQr5ijj8Ma3vQ/xSGEmuoSNYG4=">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</latexit><latexit sha1_base64="IOQr5ijj8Ma3vQ/xSGEmuoSNYG4=">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</latexit><latexit sha1_base64="IOQr5ijj8Ma3vQ/xSGEmuoSNYG4=">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</latexit>
loss

19. Dataset
: GTAV
•
: Cityscapes
•
CNN
• DeepLab, PSPNet
• DeepLab …
19
https://www.slideshare.net/DeepLearningJP2016/dlencoderdecoder-with-atrous-
separable-convolution-for-semantic-image-segmentation

20. • 2
• Target guided distillation Spatial-aware adaptation
20

21. • target
guided distillation
• spatial-aware adaptation
21

22. 22Target domain image GT dist onlybase line spt only Proposed

23. • UDA
• 2
– Target Guided Distillation:
– Spatial-Aware Adaptation:
• GTAV Cityscape
23

24. Learning to Adapt Structured Output Space for
Semantic Segmentation
24

25. Learning to Adapt Structured Output Space for Semantic Segmentation
• CVPR2018 accept
– NEC
• Visual Domain Adaptation Challenge 3
• PyTorch
– https://github.com/wasidennis/AdaptSegNet
25

26. • (Semantic Segmentation)
•
• Multi level domain adaptation
–
• ablation study
26

27. •
27

28. •
• 28

29. 29
(Cross Entropy)

30. 30
(Adversarial
Loss)

31. 31
(Multi-level Learning)

32. 32

33. •
– Discriminator
• DCGAN (5convolution, leaky ReLU)
– Segmentation Network
• DeepLab v2 (ResNet101 )
• conv4, 5
33

34. • GTA5
• 34

35. •
•
• Multi-level
•
35

36. : FCNs in theWild: Pixel-level Adversarial and Constraint-based Adaptation
•
• Global domain alignment
–
• Category specific adaptation
–
36
FCNs in theWild: Pixel-level Adversarial and Constraint-based
Adaptation [J. Hoffman 2016]

37. : CYCADA: CYCLE-CONSISTENT ADVERSARIAL DOMAIN ADAPTATION
• Cycle-GAN
37
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
[J. Hoffman 2016]

38. • Adversarial Discriminative Domain Adaptation [E. Tzeng et al. CVPR 2017]
• ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
[Y. Chen et al. CVPR2018]
• Learning to Adapt Structured Output Space for Semantic Segmentation [Y. H. Tsai
et al. CVPR2018]
• FCNs in theWild: Pixel-level Adversarial and Constraint-based Adaptation [J.
Hoffman 2016]
• CyCADA: Cycle-Consistent Adversarial Domain Adaptation [J. Hoffman 2016]
•
➡ Deep Visual Domain Adaptation: A Survey [M. Wang et al. Neurocomputing 2018]
38