3D Action Recognition
34 papers with code • 3 benchmarks • 14 datasets
Image: Rahmani et al
Benchmarks
These leaderboards are used to track progress in 3D Action Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find 3D Action Recognition models and implementationsDatasets
NTU RGB+D
BABEL
UAV-Human
Assembly101
Florence3D
Navigation Turing Test
FR-FS
3DYoga90
Fitness-AQA
MultiviewC
Subtasks
Latest papers with no code
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
CoSimGNN: Towards Large-scale Graph Similarity Computation
In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.
Combining Deep Learning Classifiers for 3D Action Recognition
The popular task of 3D human action recognition is almost exclusively solved by training deep-learning classifiers.
Mimetics: Towards Understanding Human Actions Out of Context
Our experiments show that (a) state-of-the-art 3D convolutional neural networks obtain disappointing results on such videos, highlighting the lack of true understanding of the human actions and (b) models leveraging body language via human pose are less prone to context biases.
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues
There exist a wide range of intra class variations of the same actions and inter class similarity among the actions, at the same time, which makes the action recognition in videos very challenging.
Order-Preserving Wasserstein Discriminant Analysis
Supervised dimensionality reduction for sequence data projects the observations in sequences onto a low-dimensional subspace to better separate different sequence classes.
DWnet: Deep-Wide Network for 3D Action Recognition
We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions.
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data
We achieve promising results on real conditions of this dataset with the support of data augmentation and transfer learning techniques.
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition
In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.
Localized Trajectories for 2D and 3D Action Recognition
The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion.
