Object Detection Models
U2-Net
Introduced by Qin et al. in U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object DetectionU2-Net is a two-level nested U-structure architecture that is designed for salient object detection (SOD). The architecture allows the network to go deeper, attain high resolution, without significantly increasing the memory and computation cost. This is achieved by a nested U-structure: on the bottom level, with a novel ReSidual U-block (RSU) module, which is able to extract intra-stage multi-scale features without degrading the feature map resolution; on the top level, there is a U-Net like structure, in which each stage is filled by a RSU block.
Source: U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Dichotomous Image Segmentation | 1 | 16.67% |
| Image Classification | 1 | 16.67% |
| Object Detection | 1 | 16.67% |
| RGB Salient Object Detection | 1 | 16.67% |
| Saliency Detection | 1 | 16.67% |
| Salient Object Detection | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
