Research map / Video / Video segmentation and tracking
Boosting Video Object Segmentation via Space-Time Correspondence Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add pixel- and object-level correspondence learning | STCN · DAVIS2017 val | J&F: 82.5% → 84.7% | Helped |
| Add pixel- and object-level correspondence learning | STCN · YouTube-VOS2018 val | Overall: 81.2% vs 83.6% | Helped |
| Add pixel-level correspondence learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 86.3 | Helped |
| Add object-level correspondence learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 85.9 | Helped |
| Add both pixel- and object-level correspondence learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 86.6 | Helped |
| Use inter-video negatives only for pixel-level learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 86.1 | Helped |
| Use intra-video negatives only for pixel-level learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 85.8 | Helped |
| Use both inter- and intra-video negatives for pixel-level learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 86.3 | Helped |
| Use manually annotated objects only for object-level learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 85.7 | Helped |
| Use automatically discovered objects only for object-level learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 85.8 | Helped |
| Combine manually annotated and automatically discovered objects for object-level learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4 -> 85.9 | Helped |
| Use existing correspondence-learning methods instead of the proposed VOS-specific pixel/object learning (ablation) | STCN · DAVIS2017 val | J&F: 85.4; 85.6; 85.5; 85.8 | Mixed |
Closest papers by meaning
- Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation (CVPR 2023 · similarity 0.92)
- Test-time Training for Matching-based Video Object Segmentation (NeurIPS 2023 · similarity 0.91)
- Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation (NeurIPS 2021 · similarity 0.90)
- Breaking the "Object" in Video Object Segmentation (CVPR 2023 · similarity 0.90)
- LiVOS: Light Video Object Segmentation with Gated Linear Matching (CVPR 2025 · similarity 0.88)
- Point-VOS: Pointing Up Video Object Segmentation (CVPR 2024 · similarity 0.88)
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