Feature Extractors
Receptive Field Block
Introduced by Liu et al. in Receptive Field Block Net for Accurate and Fast Object DetectionReceptive Field Block (RFB) is a module for strengthening the deep features learned from lightweight CNN models so that they can contribute to fast and accurate detectors. Specifically, RFB makes use of multi-branch pooling with varying kernels corresponding to RFs of different sizes, applies dilated convolution layers to control their eccentricities, and reshapes them to generate final representation.
Source: Receptive Field Block Net for Accurate and Fast Object Detection
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 3 | 27.27% |
| Real-Time Object Detection | 2 | 18.18% |
| Face Detection | 1 | 9.09% |
| Image Super-Resolution | 1 | 9.09% |
| Super-Resolution | 1 | 9.09% |
| BIG-bench Machine Learning | 1 | 9.09% |
| Semantic Segmentation | 1 | 9.09% |
| Clustering | 1 | 9.09% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
1x1 Convolution
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Convolutions | |
Convolution
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Convolutions | |
Dilated Convolution
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Convolutions | |
ReLU
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Activation Functions | |
Residual Connection
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Skip Connections |
