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 | DAM4SAM | EAO | 0.73 | — | Unverified |
| 2 | MCITrack-L384 | EAO | 0.62 | — | Unverified |
| 3 | SwinB-DeAOT-L | EAO | 0.62 | — | Unverified |
| 4 | MCITrack-B224 | EAO | 0.62 | — | Unverified |
| 5 | R50-DeAOT-L | EAO | 0.61 | — | Unverified |
| 6 | ODTrack-L | EAO | 0.61 | — | Unverified |
| 7 | DeAOT-S | EAO | 0.59 | — | Unverified |
| 8 | DeAOT-L | EAO | 0.59 | — | Unverified |
| 9 | SwinB-AOT-L | EAO | 0.59 | — | Unverified |
| 10 | ODTrack-B | EAO | 0.58 | — | Unverified |