Attention Mechanisms
Recurrent models of visual attention
Introduced by Mnih et al. in Recurrent Models of Visual AttentionRAM adopts RNNs and reinforcement learning (RL) to make the network learn where to pay attention.
Source: Recurrent Models of Visual Attention
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 10 | 8.55% |
| Quantization | 6 | 5.13% |
| Semantic Segmentation | 6 | 5.13% |
| Edge-computing | 5 | 4.27% |
| Retrieval | 3 | 2.56% |
| Federated Learning | 3 | 2.56% |
| Image Segmentation | 2 | 1.71% |
| Object Detection | 2 | 1.71% |
| Anomaly Detection | 2 | 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 |
