3. 調査方針
CVPR’19の論文を対象に
Domain Adaptation を Segmentation Task に適用した論文を抽出
・Strong-Weak Distribution Alignment for Adaptive Object Detection
・Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
・Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
巻末では、
予備知識として近年ポピュラーなMask-R-CNNとRoI-Alignを解説
所感:
・segmentationタスクはpixel単位でClassificationする点が特徴的
・Domain-LOSSをどの段階で,どの部分に,どの強度でかけるかが主要論点
4. Strong-Weak Distribution Alignment for Adaptive Object Detection
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko
Boston University, The University of Tokyo, RIKEN
pdf: https://arxiv.org/pdf/1812.04798.pdf (CVPR’2019)
Git: https://github.com/VisionLearningGroup/DA_Detection
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, Daniel Ulbricht
Apple Inc
pdf: ttps://arxiv.org/pdf/1903.04064.pdf (CVPR’2019)
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Yawei Luo, Liang Zheng, Tao Guan, Junqing Yu, Yi Yang
Huazhong University, Baidu Research, Australian National University
pdf: https://arxiv.org/pdf/1809.09478.pdf (CVPR’2019)
Git: https://github.com/RoyalVane/CLAN (Coming soon....)
(巻末付録)CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell
EECS and BAIR, UC Berkeley, Openai, Boston University
pdf: http://proceedings.mlr.press/v80/hoffman18a/hoffman18a.pdf (ICML’2018)
Git: https://github.com/jhoffman/cycada_release
(巻末付録)Mask R-CNN、RoI-Align
Kaiming He Georgia Gkioxari Piotr Dollar Ross Girshick
Facebook AI Research
pdf: https://arxiv.org/pdf/1703.06870.pdf
Git: https://github.com/matterport/Mask_RCNN
5. Strong-Weak Distribution Alignment
for Adaptive Object Detection (CVPR’19)
Experiment:
Task Domain Adaptation on Semantic Segmentation
Model Faster RCNN + ROI-alignment + Domain Prediction Brunch
DataSet ・Adaptation between dissimilar domain(PASCAL⇒Clipart, Watercolor)
・Adaptation between similar domain(Cityscapes⇒Foggy Cityscapes)
・Adaptation from synthetic to real images(GTA ⇒ Cityscapes)
Key Idea:
• Feature MapのH*W個の局所特徴それぞれに強めのAdversarial lossをかける
• Feature Mapの全体に対しては弱めのAdversarial lossをかける