![•
)
•
•
4
Domain Adaptive Faster R-CNN for Object Detection in the
Wild [Y. Chen et al. CVPR2018]](https://image.slidesharecdn.com/doikento20180701-180630011822/85/CVPR2018-_20180701-4-320.jpg)
![(Domain Adaptation)
• 2
5DeepVisual Domain Adaptation:A Survey [M.Wang 2018]](https://image.slidesharecdn.com/doikento20180701-180630011822/85/CVPR2018-_20180701-5-320.jpg)
![• 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]](https://image.slidesharecdn.com/doikento20180701-180630011822/85/CVPR2018-_20180701-11-320.jpg)
![: 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]](https://image.slidesharecdn.com/doikento20180701-180630011822/85/CVPR2018-_20180701-36-320.jpg)
![: CYCADA: CYCLE-CONSISTENT ADVERSARIAL DOMAIN ADAPTATION
• Cycle-GAN
37
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
[J. Hoffman 2016]](https://image.slidesharecdn.com/doikento20180701-180630011822/85/CVPR2018-_20180701-37-320.jpg)
![• 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](https://image.slidesharecdn.com/doikento20180701-180630011822/85/CVPR2018-_20180701-38-320.jpg)
CVPR2018読み会_20180701
- 1. ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes & Learning to Adapt Structured Output Space for Semantic Segmentation (@doiken23) 1 46 CVPR2018 ( )
- 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 )
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- 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
- 32. 32
- 33. • – Discriminator • DCGAN (5convolution, leaky ReLU) – Segmentation Network • DeepLab v2 (ResNet101 ) • conv4, 5 33
- 34. • GTA5 • 34
- 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