Research map / Detection and segmentation / Weakly supervised detection and segmentation
AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace HC descriptors with AnchorNet features in DSP | DSP · PASCAL Parts semantic part matching | weighted IoU: 0.41 -> 0.45 | Helped |
| replace HoG descriptors with AnchorNet features in Proposal Flow | Proposal Flow · PASCAL Parts semantic part matching | weighted IoU: 0.41 -> 0.42 | Helped |
| use class-specific AnchorNet features instead of class-agnostic features | DSP · PASCAL Parts semantic part matching | weighted IoU: 0.45 -> 0.45 | No clear effect |
| replace HC descriptors with AnchorNet features in DSP | DSP · PASCAL Parts keypoint transfer, 12 rigid classes | PCK (α = 0.05): 0.20 -> 0.23 | Helped |
| replace HoG descriptors with AnchorNet features in Proposal Flow | Proposal Flow · PASCAL Parts keypoint transfer, 12 rigid classes | PCK (α = 0.05): 0.17 -> 0.16 | No clear effect |
| remove diversity losses (ablation) | DSP + ANet-class · PASCAL Parts semantic part matching | weighted IoU: 0.45 -> 0.41 | Hurt |
| remove diversity losses (ablation) | DSP + ANet-class · PASCAL Parts keypoint transfer, 12 rigid classes | PCK (α = 0.05): 0.24 -> 0.17 | Hurt |
| replace HC descriptors with AnchorNet features | DSP · PASCAL Parts cross-instance semantic part matching | weighted IoU: 0.32 -> 0.37 | Helped |
| replace HoG descriptors with AnchorNet features | Proposal Flow · PASCAL Parts cross-instance semantic part matching | weighted IoU: 0.31 -> 0.35 | Helped |
| use AnchorNet-class instead of HoG features | NAM · PF dataset region matching | AUC for PCR: 0.29 -> 0.41 | Helped |
| use AnchorNet-class instead of HoG features | LOM · PF dataset region matching | AUC for PCR: 0.43 -> 0.46 | Helped |
| replace HoG features with AnchorNet features | Proposal Flow · AnimalParts cross-category matching | PCK (α = 0.05): 7% PCK improvement | Helped |
Closest papers by meaning
- Deep Semantic Feature Matching (CVPR 2017 · similarity 0.91)
- Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching Integration (CVPR 2024 · similarity 0.87)
- 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 0.86)
- SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations (CVPR 2025 · similarity 0.86)
- SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching (CVPR 2024 · similarity 0.86)
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