Video Panoptic Segmentation Models
ViP-DeepLab
Introduced by Qiao et al. in ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic SegmentationViP-DeepLab is a model for depth-aware video panoptic segmentation. It extends Panoptic-DeepLab by adding a depth prediction head to perform monocular depth estimation and a next-frame instance branch which regresses to the object centers in frame $t$ for frame $t + 1$. This allows the model to jointly perform video panoptic segmentation and monocular depth estimation.
Source: ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Depth Estimation | 1 | 25.00% |
| Monocular Depth Estimation | 1 | 25.00% |
| Panoptic Segmentation | 1 | 25.00% |
| Video Panoptic Segmentation | 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 |
