Methods › Computer Vision › Video Panoptic Segmentation Models › ViP-DeepLab
ViP-DeepLab
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ViP-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.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation 9 Dec 2020 · 2 repositories · arXiv:2012.05258Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Depth Estimation | 1 |
| Depth-aware Video Panoptic Segmentation | 1 |
| Monocular Depth Estimation | 1 |
| Panoptic Segmentation | 1 |
| Segmentation | 1 |
| Video Panoptic Segmentation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections