Papers › Fully Connected Object Proposals for Video Segmentation
Fully Connected Object Proposals for Video Segmentation
Federico Perazzi, Oliver Wang, Markus Gross, Alexander Sorkine-Hornung
We present a novel approach to video segmentation using multiple object proposals. The problem is formulated as a minimization of a novel energy function defined over a fully connected graph of object proposals. Our model combines appearance with long-range point tracks, which is key to ensure robustness with respect to fast motion and occlusions over longer video sequences. As opposed to previous approaches based on object proposals, we do not seek the best per-frame object hypotheses to perform the segmentation. Instead, we combine multiple, potentially imperfect proposals to improve overall segmentation accuracy and ensure robustness to outliers. Overall, the basic algorithm consists of three steps. First, we generate a very large number of object proposals for each video frame using existing techniques. Next, we perform an SVM-based pruning step to retain only high quality proposals with sufficiently discriminative power. Finally, we determine the fore- and background classification by solving for the maximum a posteriori of a fully connected conditional random field, defined using our novel energy function. Experimental results on a well established dataset demonstrate that our method compares favorably to several recent state-of-the-art approaches.
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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 | FCP | F-measure (Decay) | -1.1 | #77 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | FCP | F-measure (Mean) | 49.2 | #77 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | FCP | F-measure (Recall) | 49.5 | #77 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | FCP | J&F | 53.8 | #77 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | FCP | Jaccard (Decay) | -2.0 | #77 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | FCP | Jaccard (Mean) | 58.4 | #77 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | FCP | Jaccard (Recall) | 71.5 | #77 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.
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