Research map / Detection and segmentation / CNN features
Deep Semantic Feature Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use activation-guided key feature selection instead of EdgeBox-based selection (ablation) | Ours · PASCAL-Part, FlowWeb clusters | PCK: 0.24 -> 0.25 | Helped |
| remove spatial constraints and use nearest-neighbor matching (ablation) | Ours · PASCAL-Part, FlowWeb clusters | PCK: 0.25 -> 0.15 | Hurt |
| add the binary objectness potential (ablation) | Ours · Proposal Flow | PCK: 0.66 -> 0.68 | Helped |
| add unary and binary objectness potentials (ablation) | Ours · Proposal Flow | PCK: 0.62 -> 0.68 | Helped |
| use the proposed dense matching method instead of LOM | semantic matching · Proposal Flow | PCK: 0.56 -> 0.68 | Helped |
| use the proposed matching method instead of Flow Web | semantic matching · PASCAL-Part | IOU: 0.43 -> 0.43 | No clear effect |
Closest papers by meaning
- AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching (CVPR 2017 · similarity 0.91)
- Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching Integration (CVPR 2024 · similarity 0.89)
- MATCHA: Towards Matching Anything (CVPR 2025 · similarity 0.87)
- Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence (ICLR 2024 · similarity 0.87)
- SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching (CVPR 2024 · similarity 0.87)
- Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence (NeurIPS 2025 · similarity 0.87)
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