Papers › CRVOS: Clue Refining Network for Video Object Segmentation
CRVOS: Clue Refining Network for Video Object Segmentation
Suhwan Cho, MyeongAh Cho, Tae-young Chung, Heansung Lee, Sangyoun Lee
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of them have complex intermediate networks which generate strong specifiers to be robust against challenging scenarios, and this is quite inefficient when dealing with relatively simple scenarios. To solve this problem, we propose a real-time network, Clue Refining Network for Video Object Segmentation (CRVOS), that does not have any intermediate network to efficiently deal with these scenarios. In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame's coarse mask and coordinates information. We also propose a novel refine module which shows the better performance compared with the general ones by using a deconvolution layer instead of a bilinear upsampling layer. Our proposed method shows the fastest speed among the existing methods with a competitive accuracy. On DAVIS 2016 validation set, our method achieves 63.5 fps and J&F score of 81.6%.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | F-measure (Decay) | 8.8 | #60 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | F-measure (Mean) | 81.0 | #60 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | F-measure (Recall) | 90.3 | #60 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | J&F | 81.6 | #60 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | Jaccard (Decay) | 10.0 | #60 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | Jaccard (Mean) | 82.2 | #60 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | CRVOS | Jaccard (Recall) | 93.9 | #60 of 78 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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