| FORTH-ModelBasedTracker/MocapNET |
738 |
|
0 |
0 |
over 2 years ago |
0 |
|
16 |
other |
C++ |
| We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance |
| alvinwan/neural-backed-decision-trees |
445 |
|
0 |
0 |
almost 5 years ago |
0 |
|
8 |
mit |
Python |
| Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet |
| kunglab/ddnn |
70 |
|
0 |
0 |
over 7 years ago |
0 |
|
2 |
|
Python |
| thunlp/DIAG-NRE |
47 |
|
0 |
0 |
almost 7 years ago |
0 |
|
1 |
mit |
Python |
| Source code for ACL 2019 paper "DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction". |
| billy-inn/NFETC |
28 |
|
0 |
0 |
almost 6 years ago |
0 |
|
2 |
mit |
Python |
| Neural Fine-grained Entity Type Classification https://arxiv.org/abs/1803.03378 |
| mikebenfield/Learned-Index-Structures |
24 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
|
Rust |
| Learned Index Structures |
