Research map / Detection and segmentation / Open-set and few-shot detection
SCOPS: Self-Supervised Co-Part Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add semantic consistency loss (ablation) | SCOPS · CelebA unaligned | landmark error (%): 28.49 -> 15.01 | Helped |
| add geometric concentration loss (ablation) | SCOPS · CelebA unaligned | landmark error (%): 21.85 -> 15.01 | Helped |
| add equivariance loss (ablation) | SCOPS · CelebA unaligned | landmark error (%): 18.60 -> 15.01 | Helped |
| add saliency constraint (ablation) | SCOPS · CelebA unaligned | landmark error (%): 22.11 -> 15.01 | Helped |
| use semantic consistency alone instead of the full loss combination (ablation) | SCOPS · CelebA unaligned | landmark error (%): 15.01 -> 23.53 | Hurt |
| increase the part count from K=2 to K=8 (ablation) | SCOPS · CelebA unaligned | landmark error (%): 25.46 -> 15.01 | Helped |
| increase the part count from K=8 to K=10 (ablation) | SCOPS · CelebA unaligned | landmark error (%): 15.01 -> 14.71 | Helped |
| use projective or thin-plate-spline spatial transforms instead of similarity transforms (ablation) | SCOPS equivariance training · part segmentation | part segmentation performance | No clear effect |
| add CRF post-processing (ablation) | SCOPS · PASCAL VOC | cosegmentation IoU: 55.76 -> 57.92; 60.79 -> 62.70; 56.95 -> 58.17; 69.02 -> 80.54; 73.82 -> 75.32; 65.18 -> 66.14; 58.53 -> 59.15 | Helped |
| replace DFF with SCOPS | part segmentation and cosegmentation evaluation · PASCAL VOC | cosegmentation IoU: 50.96 -> 57.92; 57.64 -> 62.70; 52.29 -> 58.17; 50.87 -> 80.54; 58.64 -> 75.32; 57.56 -> 66.14; 55.86 -> 59.15 | Helped |
Closest papers by meaning
- Self-Supervised Learning of Object Parts for Semantic Segmentation (CVPR 2022 · similarity 0.90)
- Unsupervised Co-part Segmentation through Assembly (ICML 2021 · similarity 0.90)
- CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models (CVPR 2025 · similarity 0.86)
- Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations (CVPR 2024 · similarity 0.86)
- Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation (CVPR 2025 · similarity 0.86)
- Multi-Part Object Representations via Graph Structures and Co-Part Discovery (NeurIPS 2026 · similarity 0.86)
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