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Convolutional Pose Machines
- 2. 緒方 貴紀 (twitter: @conta_) CTO@ABEJA, Inc. Computer Visionとか、Machine Learningを使った プロダクト開発をやっています。 Self Introduction
- 4. Related Work Pictorial structures Hierarchical models Sequential prediction Convolutional architectures [A. Toshev and C. Szegedy, CVPR’2013] [Tian et al., ICCV’2011][Mykhaylo et al., CVPR’2009] [Ramakrishna et al., 2014]
- 5. Related Work Pictorial structures Hierarchical models Sequential prediction Convolutional architectures [A. Toshev and C. Szegedy, CVPR’2013] [Tian et al., ICCV’2011][Mykhaylo et al., CVPR’2009] [Ramakrishna et al., 2014]
- 6. Pose Machines? Confidence Maps [Ramakrishna et al., 2014]
- 7. Pose Machines [Ramakrishna et al., 2014] パッチから特徴量を抽出し、各Parts or NotのClassifier を用いて、Confidence Mapsを作りたい
- 8. Pose Machines [Ramakrishna et al., 2014] 局所的な特徴を用いた推定はWeak Partsによっては、局所的特徴だど推定できない。。。
- 9. Part Contextは非常に有効な特徴 Pose Machines [Ramakrishna et al., 2014] 前段階での推定結果を用いて、各Partsの関係性を 事前情報無しにを活用出来ないか？
- 10. Pose Machines Stage I Confidence Head Neck L-Shoulder L-Elbow L-Wrist g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence Maps Image Features [Ramakrishna et al., 2014]
- 11. Pose Machines Stage II Confidence g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence Maps Image Features Head Neck L-Shoulder L-Elbow L-Wrist [Ramakrishna et al., 2014]
- 12. Pose Machines Stage III Confidence Head Neck L-Shoulder L-Elbow L-Wrist g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence Maps Image Features Head Neck L-Shoulder L-Elbow L-Wrist [Ramakrishna et al., 2014]
- 13. Pose Machines Stage III Confidence Head Neck L-Shoulder L-Elbow L-Wrist g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence Maps Image Features Head Neck L-Shoulder L-Elbow L-Wrist [Ramakrishna et al., 2014]
- 14. Pose Machines (Previous Work)
- 15. Pose Machines (Previous Work) HoG Random Forests
- 16. Convolutional Pose Machines (CPM) Deep Deep
- 17. Architecture of CPM 階層的なCNNによるPose Machinesの実現
- 19. 各Parts + BackgroundのConfidence Maps (P+1) ＊MPII Human Pose Datasetだと P=14 Outputs
- 20. Stage 2 Stage2: Localな特徴量 + Part Contextによる学習
- 22. 各StageのConfidence Mapsと教師データとのEuclidean Distance Loss 教師データ: 各PartsのGround truth locationからGaussian Peakを計算したもの Loss Function
- 23. Stage2以降、全段階のConfidence Mapのおかげで 良い推定ができている Spatial context from belief maps
- 24. 3つのDatasetsで実験 - MPII Human Pose Dataset - Leeds Sports Pose (LSP) Datase - FLIC Dataset Experiments
- 25. Results
- 29. 実装してみた
- 30. 1. (色々頑張って実験した結果)いい感じの連続構成 CNNによって、暗黙的な空間モデルの学習ができた 2. Graphical Modelによる推論無しに、階層構造の Predictionができた Conclusion
- 31. We are hiring! → https://www.wantedly.com/ companies/abeja 博士持ち大歓迎！