Semi-Supervised Video Object Segmentation
The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
Papers
Showing 1–10 of 147 papers
All datasetsDAVIS 2017 (val)DAVIS 2016DAVIS-2017 (test-dev)YouTube-VOS 2018DAVIS (no YouTube-VOS training)YouTube-VOS 2019VOT2020MOSELong Video DatasetYouTubeDAVIS 2017BURST-test
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ISVOS | J&F | 90 | — | Unverified |
| 2 | READMem-MiVOS (sr=10) | J&F | 86 | — | Unverified |
| 3 | READMem-QDMN (sr=1) | J&F | 84.3 | — | Unverified |
| 4 | READMem-QDMN (sr=10) | J&F | 84 | — | Unverified |
| 5 | AFB-URR | J&F | 83.7 | — | Unverified |
| 6 | READMem-MiVOS (s=1) | J&F | 83.6 | — | Unverified |
| 7 | READMem-STCN (sr=10) | J&F | 81.8 | — | Unverified |
| 8 | READMem-STCN (sr=1) | J&F | 80.8 | — | Unverified |