Object Detection Models
GCNet
Introduced by Cao et al. in GCNet: Non-local Networks Meet Squeeze-Excitation Networks and BeyondA Global Context Network, or GCNet, utilises global context blocks to model long-range dependencies in images. It is based on the Non-Local Network, but it modifies the architecture so less computation is required. Global context blocks are applied to multiple layers in a backbone network to construct the GCNet.
Source: GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 3 | 20.00% |
| Stereo Matching | 2 | 13.33% |
| Instance Segmentation | 2 | 13.33% |
| Point Cloud Registration | 1 | 6.67% |
| Metric Learning | 1 | 6.67% |
| Robot Navigation | 1 | 6.67% |
| Management | 1 | 6.67% |
| Multi-Object Tracking | 1 | 6.67% |
| Object Tracking | 1 | 6.67% |
Global Context Block
