Papers › Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
Yuk Heo, Yeong Jun Koh, Chang-Su Kim
We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
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Code
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Code Syntology ran Syntology
15 samples harvested; 6 ran; 0 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Interactive Video Object Segmentation | DAVIS 2017 | GIS | AUC-J | 0.820 | #2 of 7 | Archive leaderboard | report |
| Interactive Video Object Segmentation | DAVIS 2017 | GIS | AUC-J&F | 0.856 | #2 of 7 | Archive leaderboard | report |
| Interactive Video Object Segmentation | DAVIS 2017 | GIS | J&F@60s | 0.866 | #2 of 7 | Archive leaderboard | report |
| Interactive Video Object Segmentation | DAVIS 2017 | GIS | J@60s | 0.829 | #2 of 7 | 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.
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