Semantic Segmentation Models
ESPNet
Introduced by Mehta et al. in ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic SegmentationESPNet is a convolutional neural network for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power.
Source: ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Speech Recognition | 10 | 25.64% |
| Automatic Speech Recognition | 8 | 20.51% |
| Semantic Segmentation | 5 | 12.82% |
| Speech Separation | 2 | 5.13% |
| Real-Time Semantic Segmentation | 2 | 5.13% |
| Robust Speech Recognition | 1 | 2.56% |
| Speech Enhancement | 1 | 2.56% |
| Spoken Language Understanding | 1 | 2.56% |
| Speaker Diarization | 1 | 2.56% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
1x1 Convolution
|
Convolutions | |
Convolution
|
Convolutions | |
ESP
|
Image Model Blocks | |
Kaiming Initialization
|
Initialization | |
PReLU
|
Activation Functions |
