CVPR19-Graphonomy- Universal Human Parsing via Graph Transfer Learning

1. Paper list: • CVPR19-Graphonomy- Universal Human Parsing via Graph Transfer Learning • AAAI18-Spaital Temporal Graph Convolutional Networks for Skeleton-based Action Recognition • BMVC18-Part-based Graph Convolutional Network for Action Recognition • CVPR19-Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition • CVPR19-An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition • NIPS2017-Inductive Representation Learning on Large Graphs • CVPR19-Graphical Contrastive Losses for Scene Graph Generation • ECCV18-PersonLab- Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model • MM18-RGCNN- Regularized Graph CNN for Point Cloud Segmentation • WWW19-Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations • AAAI19-Multi-GCN- Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

2. CVPR2019

3. Human Parsing: huge different granularity and quantity of semantic labels • A single universal human parsing model to tackle all levels of the task(Multi-task learning) • Pretrain in one dataset, transfer to another dataset with graph transfer capability(Transfer learning)

4. Intra-Graph Reasoning get local feature tensors from convolution layers construct graph with external structure knowledge feature maps -> graph node feature employ graph convolution three times re-project the graph nodes to image features

5. Intra-Graph Reasoning get local feature tensors from convolution layers construct graph with external structure knowledge feature maps -> graph node feature employ graph convolution three times re-project the graph nodes to image features

6. Inter-Graph Transfer sourcegraph ; target graph Transfer matrix: Handcraft relation: hard weight (0,1) Learnable matrix: random initialize the transfer matrix Feature similarity: computing the similarity between nodes Semantic similarity: explore linguistic knowledge

9. AAAI18

12. BMVC18

15. Geometricfeatures:relativecoordinates Temporalfeatures:temporaldisplacements

16. CVPR19

18. Graph Convolutional Neural Network

19. Attention Enhanced Graph Convolutional LSTM

20. Attention Enhanced Graph Convolutional LSTM

21. AGC-LSTMNetwork Joints Feature Representation Temporal Hierarchical Architecture: average pooling in temporal domain to increase the temporal receptive field of the top AGC-LSTM layers Learning AGC-LSTM

23. CVPR19

25. Spatio-Temporal GCN

26. Actional-Structural GCN

27. Actional-Structural GCN

28. Actional-Structural Graph Convolution Block

30. CVPR19

38. NIPS2017

43. ECCV2018

44. Unified manner: • multi-person detection • 2D pose estimation • instance segmentation • TO DO: • identify person instance • localize facial and body keypoint • estimate instance segmentation mask

46. Keypoint detection Produce heatmaps (one channel per keypoint), offsets(two channels per keypoint for displacements in the horizontal and vertical directions) points from the image position x to the k-th keypoint of the closest person instance j

47. Hough voting

48. Grouping keypoints into person detection instances • Mid-range pairwise offsets • Recurrent offset refinement • Fast greedy decoding

49. Keypoint- and instance-level detection scoring

50. Instance-level person segmentation points from the image position x to the position of the k-th keypoint of the corresponding instance j