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 | 13.64% |
| Decoder | 2 | 9.09% |
| Stereo Matching | 2 | 9.09% |
| Instance Segmentation | 2 | 9.09% |
| Real-Time Semantic Segmentation | 1 | 4.55% |
| Semantic Segmentation | 1 | 4.55% |
| Graph Neural Network | 1 | 4.55% |
| Prediction | 1 | 4.55% |
| Point Cloud Registration | 1 | 4.55% |
Global Context Block
