Research map / Video / Video segmentation and tracking
Reciprocal Transformations for Unsupervised Video Object Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RTNet instead of MATNet | UVOS · DAVIS-16 | J: 82.4 -> 85.6 | Helped |
| Use RTNet instead of COSNet | UVOS · YouTube-Objects | mean J: 70.5 -> 71.0 | Helped |
| Add intra-frame salient-object transformation (ablation) | RTNet · DAVIS-16 | Mean J: 77.53 -> 78.19 | Helped |
| Add moving-object transformation (ablation) | RTNet · DAVIS-16 | Mean J: 77.53 -> 83.01 | Helped |
| Add recurring-object temporal-coherence transformation (ablation) | RTNet · DAVIS-16 | Mean J: 77.53 -> 79.57 | Helped |
| Add all three candidate-object transformations (ablation) | RTNet · DAVIS-16 | Mean J: 77.53 -> 83.96 | Helped |
| Mutually transform appearance and motion features rather than transform motion toward appearance only (ablation) | RTNet · DAVIS-16 | Mean J: 83.01 -> 83.74 | Helped |
| Add STAFM for attentive appearance-motion fusion (ablation) | RTNet · DAVIS-16 | Mean J: 84.15 -> 84.31 | Helped |
| Add STAFM for attentive appearance-motion fusion (ablation) | RTNet · DAVIS-16 | Mean F: 82.89 -> 83.01 | Helped |
Closest papers by meaning
- Learning Unsupervised Video Object Segmentation Through Visual Attention (CVPR 2019 · similarity 0.89)
- Breaking the "Object" in Video Object Segmentation (CVPR 2023 · similarity 0.89)
- Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier (AAAI 2022 · similarity 0.88)
- Dual Prototype Attention for Unsupervised Video Object Segmentation (CVPR 2024 · similarity 0.87)
- Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation (AAAI 2024 · similarity 0.86)
- M^3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation (CVPR 2025 · similarity 0.86)
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