Research map / Video / Video segmentation and tracking
Video Segmentation via Multiple Granularity Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Multiple Granularity Analysis instead of the strongest listed baseline [38] | video segmentation · Segtrack v2 | overlap ratio (mean per object, %): 74.1 -> 75.5 | Helped |
| Use Multiple Granularity Analysis instead of the strongest listed baseline [38] | video segmentation · Segtrack v2 | overlap ratio (mean per sequence, %): 76.4 -> 77.6 | Helped |
| Use Multiple Granularity Analysis instead of the strongest listed baseline [2] | video segmentation · DAVIS | overlap ratio (Mean, %): 72.4 -> 75.2 | Helped |
| Add pixel-level graph-cut refinement to MIL superpixel segmentation (ablation) | video segmentation · Girl | overlap ratio (%): 70.1% -> 87.2% | Helped |
| Add pixel-level graph-cut refinement to MIL superpixel segmentation (ablation) | video segmentation · Parachute | overlap ratio (%): 74.2% -> 94.6% | Helped |
| Add pixel-level graph-cut refinement to MIL superpixel segmentation (ablation) | video segmentation · Monkeydog-M | overlap ratio (%): 30% -> 59.7% | Helped |
| Add pixel-level graph-cut refinement to MIL superpixel segmentation (ablation) | video segmentation · Blackswan | overlap ratio (%): 73.2% -> 94.7% | Helped |
| Add pixel-level graph-cut refinement to MIL superpixel segmentation (ablation) | video segmentation · Hike | overlap ratio (%): 77.9% -> 93.2% | Helped |
| Add pixel-level graph-cut refinement to MIL superpixel segmentation (ablation) | video segmentation · Stroller | overlap ratio (%): 17.2% -> 30.0% | Helped |
Closest papers by meaning
- Instance-Level Video Segmentation From Object Tracks (CVPR 2016 · similarity 0.88)
- Multi-Granularity Video Object Segmentation (AAAI 2025 · similarity 0.87)
- SMITE: Segment Me In TimE (ICLR 2025 · similarity 0.85)
- TrackIME: Enhanced Video Point Tracking via Instance Motion Estimation (NeurIPS 2024 · similarity 0.84)
- Robust and Consistent Online Video Instance Segmentation via Instance Mask Propagation (AAAI 2025 · similarity 0.84)
- Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation (CVPR 2025 · similarity 0.84)
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