Research map / Video / Video segmentation and tracking
Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add iterative growing with detector updates on temporally adjacent frames (ablation) | Track and Segment · Visual Tracker Benchmark | OPE success plot AUC: 0.319 -> 0.365 | Helped |
| Add segmentation-based refinement of bounding boxes (ablation) | Track and Segment · Visual Tracker Benchmark | OPE success plot AUC: 0.365 -> 0.437 | Helped |
Closest papers by meaning
- Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation (CVPR 2021 · similarity 0.91)
- Self-supervised Object-Centric Learning for Videos (NeurIPS 2023 · similarity 0.89)
- Track, Check, Repeat: An EM Approach to Unsupervised Tracking (CVPR 2021 · similarity 0.88)
- TrackIME: Enhanced Video Point Tracking via Instance Motion Estimation (NeurIPS 2024 · similarity 0.87)
- Learning Segmentation from Point Trajectories (NeurIPS 2024 · similarity 0.87)
- Guided Slot Attention for Unsupervised Video Object Segmentation (CVPR 2024 · similarity 0.87)
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