Video Object Segmentation Models
VOS
Introduced by Robinson et al. in Learning Fast and Robust Target Models for Video Object SegmentationVOS is a type of video object segmentation model consisting of two network components. The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation. The segmentation model is exclusively trained offline, designed to process the coarse scores into high quality segmentation masks.
Source: Learning Fast and Robust Target Models for Video Object Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Video Object Segmentation | 102 | 18.85% |
| Semantic Segmentation | 101 | 18.67% |
| Video Semantic Segmentation | 100 | 18.48% |
| Object | 67 | 12.38% |
| Semi-Supervised Video Object Segmentation | 33 | 6.10% |
| Optical Flow Estimation | 13 | 2.40% |
| Unsupervised Video Object Segmentation | 8 | 1.48% |
| Visual Object Tracking | 8 | 1.48% |
| Object Detection | 7 | 1.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
