Research map / Detection and segmentation / CNN features
Deep Object Co-Segmentation via Spatial-Semantic Network Modulation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the spatial modulator (ablation) | Full co-segmentation model · PASCAL-VOC | Ave. P (%): 94.5 -> 94.9 | Helped |
| Add the spatial modulator (ablation) | Full co-segmentation model · PASCAL-VOC | Ave. J (%): 69 -> 71 | Helped |
| Add the semantic modulator (ablation) | Full co-segmentation model · PASCAL-VOC | Ave. P (%): 85.0 -> 94.9 | Helped |
| Add the semantic modulator (ablation) | Full co-segmentation model · PASCAL-VOC | Ave. J (%): 38 -> 71 | Helped |
| Add both spatial and semantic modulators (ablation) | Segmentation network without either modulator · PASCAL-VOC | Ave. P (%): 82.0 -> 94.9 | Helped |
| Add both spatial and semantic modulators (ablation) | Segmentation network without either modulator · PASCAL-VOC | Ave. J (%): 27 -> 71 | Helped |
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- Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling (CVPR 2024 · similarity 0.89)
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- DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection (CVPR 2019 · similarity 0.88)
- Self-Supervised Learning of Object Parts for Semantic Segmentation (CVPR 2022 · similarity 0.88)
- Learning Spatial-Semantic Features for Robust Video Object Segmentation (ICLR 2025 · similarity 0.87)
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