Paper:

Newell, Alejandro, Zhiao Huang, and Jia Deng. “Associative embedding: End-to-end learning for joint detection and grouping.” Advances in Neural Information Processing Systems. 2017.

• Performance
• Basics
  • Associative embedding
  • Jointly perform detections and grouping using a single-stage deep network trained end-to-end
  • For each detection, introduce a “tag” (is a number) to identify which group this detection belongs to.
    • Note: We have no ground truth tags for the network to predict, because what matters is not the particular tag values, only the difference between them.
  • Output: Two heatmaps
    • A heatmap for Per-pixel detection scores. (detection score at each pixel for each joint.)
    • A heatmap for per-pixel identity tags.(tagging score at each pixel for each joint.)
    • For multi-person pose estimation, output a detection heatmap and a tagging heatmap for each body joint, then group body joints with similar tags into individual people.
  • Two loss functions together
    • Detection loss: mean square error (MSE) between each predicted detection heatmap and its ground truth heatmap (is a 2D Gaussian activation at each keypoint location).
    • Grouping loss: We compare the tags within each person and across people, Tags within a person should be the same, while tags across people should be different.

• Other methods mentioned.
  • Vector embedding
  • Perceptual organization: group pixels of an image into regions, parts and objects.
  • Multiplerson pose estimation
  • Instance segmentation
• Evaluation
  • Dataset
    • MPII human multi-person http://human-pose.mpi-inf.mpg.de/
      • 25K images containing over 40K people with annotated body joints. Covers 410 human activities.
    • COCO 2016 keypoints challenge. http://cocodataset.org/#keypoints-challenge2017
  • Evaluation metrics
    • Average precision (AP)
• Questions: How to get the tags of training data?

Paper:

Zhao, Ruiqi, Yan Wang, and Aleix M. Martinez. “A simple, fast and highly-accurate algorithm to recover 3d shape from 2d landmarks on a single image.” IEEE Transactions on Pattern Analysis and Machine Intelligence (2017).

Paper:

Zhou, Xingyi, et al. “Deep kinematic pose regression.” European Conference on Computer Vision. Springer, Cham, 2016.

Paper:

Tekin, Bugra, et al. “Structured prediction of 3d human pose with deep neural networks.” arXiv preprint arXiv:1605.05180(2016).

Paper:

Chen, Xianjie, and Alan Yuille. “Parsing occluded people by flexible compositions.” Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015.