Convolutions
3D Convolution
A 3D Convolution is a type of convolution where the kernel slides in 3 dimensions as opposed to 2 dimensions with 2D convolutions. One example use case is medical imaging where a model is constructed using 3D image slices. Additionally video based data has an additional temporal dimension over images making it suitable for this module.
Image: Lung nodule detection based on 3D convolutional neural networks, Fan et al
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Action Recognition | 30 | 7.33% |
| Semantic Segmentation | 18 | 4.40% |
| Temporal Action Localization | 15 | 3.67% |
| Object Detection | 13 | 3.18% |
| Image Classification | 12 | 2.93% |
| Super-Resolution | 9 | 2.20% |
| Autonomous Driving | 8 | 1.96% |
| Video Understanding | 7 | 1.71% |
| Depth Estimation | 7 | 1.71% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
