Convolutional Neural Networks
VoVNetV2
Introduced by Lee et al. in CenterMask : Real-Time Anchor-Free Instance SegmentationVoVNetV2 is a convolutional neural network that improves upon VoVNet with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNets and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of the original squeeze-and-excitation module.
Source: CenterMask : Real-Time Anchor-Free Instance Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Instance Segmentation | 1 | 14.29% |
| Object Detection | 1 | 14.29% |
| Panoptic Segmentation | 1 | 14.29% |
| Real-time Instance Segmentation | 1 | 14.29% |
| Real-Time Object Detection | 1 | 14.29% |
| Semantic Segmentation | 1 | 14.29% |
| Semi-Supervised Instance Segmentation | 1 | 14.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Convolution
|
Convolutions | |
Max Pooling
|
Pooling Operations | |
OSA (identity mapping + eSE)
|
Skip Connection Blocks |
