Research map / Architectures and efficiency / Attention and transformers
Learning to Match Aerial Images With Deep Attentive Architectures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add Spatial Transformer patch proposals to Hybrid CNN (ablation) | Hybrid CNN · aerial | accuracy: .909 -> .926 | Helped |
| remove pool5 and replace conv5 with a 1 × 1 dimensionality-reduction convolution (ablation) | Hybrid CNN · aerial | accuracy: .881 -> .909 | Helped |
| add Spatial Transformer patch proposals to Hybrid CNN (ablation) | Hybrid CNN · aerial | average precision: .963 -> .975 | Helped |
| remove pool5 and replace conv5 with a 1 × 1 dimensionality-reduction convolution (ablation) | Hybrid CNN · aerial | average precision: .942 -> .963 | Helped |
| add Spatial Transformer patch proposals to Hybrid CNN (ablation) | Hybrid CNN · Lausanne | accuracy: .959 -> .959 | No clear effect |
| add Spatial Transformer patch proposals to Hybrid CNN (ablation) | Hybrid CNN · Lausanne | average precision: .992 -> .992 | No clear effect |
Closest papers by meaning
- XFeat: Accelerated Features for Lightweight Image Matching (CVPR 2024 · similarity 0.84)
- OmniGlue: Generalizable Feature Matching with Foundation Model Guidance (CVPR 2024 · similarity 0.84)
- Multiview Aerial Visual RECognition (MAVREC): Can Multi-view Improve Aerial Visual Perception? (CVPR 2024 · similarity 0.83)
- ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses (NeurIPS 2024 · similarity 0.83)
- GIM: Learning Generalizable Image Matcher From Internet Videos (ICLR 2024 · similarity 0.83)
- UFM: A Simple Path towards Unified Dense Correspondence with Flow (NeurIPS 2025 · similarity 0.83)
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