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
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Action Recognition | 30 | 7.01% |
Semantic Segmentation | 19 | 4.44% |
Temporal Action Localization | 15 | 3.50% |
Object Detection | 13 | 3.04% |
Image Classification | 12 | 2.80% |
Super-Resolution | 9 | 2.10% |
Depth Estimation | 8 | 1.87% |
Autonomous Driving | 8 | 1.87% |
Stereo Matching | 7 | 1.64% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |