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 | 7.75% |
| Semantic Segmentation | 17 | 4.39% |
| Temporal Action Localization | 15 | 3.88% |
| Image Classification | 12 | 3.10% |
| Object Detection | 12 | 3.10% |
| Super-Resolution | 9 | 2.33% |
| Depth Estimation | 7 | 1.81% |
| Denoising | 7 | 1.81% |
| Autonomous Driving | 7 | 1.81% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
