Research map / Graph learning / Graph algorithms and clustering
Guided Similarity Separation for Image Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add spatial verification when building the training adjacency matrix (GSSV vs GSS) (ablation) | GeM+GSS · ROxford Hard | mAP: 57.5 -> 62.2 | Helped |
| Add spatial verification at query time on top of GSSV (ablation) | GeM+GSSV · ROxford Hard | mAP: 62.2 -> 64.7 | Helped |
| Add spatial verification at query time on top of GSSV (ablation) | GeM+GSSV · RParis Hard | mAP: 85.3 -> 85.3 | No clear effect |
| Replace GeM descriptors with GSS (GCN neighbor encoding plus pairwise separation loss) | GeM · ROxford Hard | mAP: 38.5 -> 57.5 | Helped |
| GSS vs best similarity propagation baseline (GeM+DSM+DFS) | GeM · RParis Hard | mAP: 79.3 -> 83.5 | Helped |
| Spatial verification at query time costs more inference time for more candidates (ablation) | GSSV-SV · ROxford Hard | run-time overhead: over 70% increase in run-time | Hurt |
Closest papers by meaning
- Scene Graph Embeddings Using Relative Similarity Supervision (AAAI 2021 · similarity 0.90)
- Explore-Exploit Graph Traversal for Image Retrieval (CVPR 2019 · similarity 0.89)
- SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval (ICML 2025 · similarity 0.89)
- Fast Spectral Ranking for Similarity Search (CVPR 2018 · similarity 0.89)
- Instance-Level Composed Image Retrieval (NeurIPS 2025 · similarity 0.87)
- Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval (ICLR 2025 · similarity 0.86)
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