Study Notes

1.Notes for Paper “Detailed 3D Representations for Object Recognition and Modeling”

2.Notes for “A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images”

3.Survey and Summary for Datasets

4.Survey and summary for 2D feature descriptors

5.Notes for Paper “Associative Embedding: End-to-End Learning for Joint Detection and Grouping”

6.Notes for Paper “A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image”

7.Notes for Paper “Deep Kinematic Pose Regression”

8.Notes for Paper “Structured prediction of 3d human pose with deep neural networks”

9.Notes for Paper “Parsing Occluded People by Flexible Compositions”

10.Notes for Paper “Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations”

11.Notes for Paper “Compositional human pose regression”

12.Notes for Paper “A limb based graphical model for human pose estimation”

13.Notes for Paper “Towards Accurate Multi-person Pose Estimation in the Wild “

14.Notes for Paper “Adversarial posenet: A structure-aware convolutional network for human pose estimation”

15Notes for Paper “Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields”

16.Notes for Paper “MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild”

17.Notes for Paper “Stacked Hourglass Networks for Human Pose Estimation”

18.Notes for Paper “Recurrent network models for human dynamics”

19.Notes for Paper “Lattice Long Short-Term Memory for Human Action Recognition”

20.Notes for Paper “Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks”

21.Notes for Paper “Spatiotemporal Pyramid Network for Video Action Recognition”

22.Notes for Paper “Unsupervised Learning of Video Representations using LSTMs”

23.Notes for paper “Long term RNN for recognition and description”

24.Notes for Paper “Two stream method for action recognition in videos”