Semantic Segmentation Models
CascadePSP
Introduced by Cheng et al. in CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local RefinementCascadePSP is a general segmentation refinement model that refines any given segmentation from low to high resolution. The model takes as input an initial mask that can be an output of any algorithm to provide a rough object location. Then the CascadePSP will output a refined mask. The model is designed in a cascade fashion that generates refined segmentation in a coarse-to-fine manner. Coarse outputs from the early levels predict object structure which will be used as input to the latter levels to refine boundary details.
Source: CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 2 | 50.00% |
| Video Semantic Segmentation | 1 | 25.00% |
| Scene Parsing | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
