Attention Modules
Triplet Attention
Introduced by Misra et al. in Rotate to Attend: Convolutional Triplet Attention ModuleTriplet attention comprises of three branches each responsible for capturing crossdimension between the spatial dimensions and channel dimension of the input. Given an input tensor with shape (C × H × W), each branch is responsible for aggregating cross-dimensional interactive features between either the spatial dimension H or W and the channel dimension C.
Source: Rotate to Attend: Convolutional Triplet Attention Module
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Drug Discovery | 2 | 10.00% |
| Object Detection | 2 | 10.00% |
| Graph Learning | 1 | 5.00% |
| Graph Property Prediction | 1 | 5.00% |
| Graph Regression | 1 | 5.00% |
| Link Prediction | 1 | 5.00% |
| Molecular Property Prediction | 1 | 5.00% |
| Property Prediction | 1 | 5.00% |
| Computational Efficiency | 1 | 5.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
