Research map / Detection and segmentation / CNN features
SFNet: Learning Object-Aware Semantic Correspondence
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use object-aware semantic flow with foreground-mask training | ResNet-101 · PF-WILLOW | PCK (α=0.1): 0.71 -> 0.74 | Helped |
| Use object-aware semantic flow with foreground-mask training | ResNet-101 · PF-PASCAL test split | PCK (α=0.1): 0.72 -> 0.79 | Helped |
| Use object-aware semantic flow with foreground-mask training | ResNet-101 · Caltech-101 | LT-ACC: 0.85 -> 0.88 | Helped |
| Use object-aware semantic flow with foreground-mask training | ResNet-101 · Caltech-101 | IoU: 0.63 -> 0.67 | Helped |
| Add flow consistency to mask consistency (ablation) | SFNet · PF-PASCAL test split | PCK (α=0.1): 0.675 -> 0.782 | Helped |
| Add smoothness loss to mask and flow consistency (ablation) | SFNet · PF-PASCAL test split | PCK (α=0.1): 0.782 -> 0.787 | Helped |
| Add adaptation layers (ablation) | ResNet-101 semantic correspondence features · PF-PASCAL test split | PCK (α=0.1): 0.458 -> 0.725 | Helped |
| Replace soft argmax with kernel soft argmax (ablation) | Multi-level SFNet · PF-PASCAL test split | PCK (α=0.1): 0.762 -> 0.787 | Helped |
| Add deeper conv5-3 features to adapted conv4-23 features (ablation) | SFNet · PF-PASCAL test split | PCK (α=0.1): 0.750 -> 0.787 | Helped |
| Use soft argmax directly without adaptation (ablation) | ResNet-101 baseline · PF-PASCAL test split | PCK (α=0.1): 0.458 -> 0.088 | Hurt |
Closest papers by meaning
- Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching Integration (CVPR 2024 · similarity 0.88)
- SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations (CVPR 2025 · similarity 0.88)
- Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence (NeurIPS 2025 · similarity 0.87)
- Bridging Viewpoint Gaps: Geometric Reasoning Boosts Semantic Correspondence (CVPR 2025 · similarity 0.87)
- Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence (CVPR 2024 · similarity 0.86)
- Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps (CVPR 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.