Semantic Segmentation Models
Boundary-Aware Segmentation Network
Introduced by Qin et al. in Boundary-Aware Segmentation Network for Mobile and Web ApplicationsBASNet, or Boundary-Aware Segmentation Network, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.
Source: Boundary-Aware Segmentation Network for Mobile and Web Applications
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 1 | 12.50% |
| Salient Object Detection | 1 | 12.50% |
| Action Localization | 1 | 12.50% |
| Temporal Action Localization | 1 | 12.50% |
| Weakly-supervised Temporal Action Localization | 1 | 12.50% |
| Camouflaged Object Segmentation | 1 | 12.50% |
| Image Segmentation | 1 | 12.50% |
| Semantic Segmentation | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
