AI Paper Atlas

Research map / Vision-language and multimodal / Human and embodied modeling

Rethinking Visual Geo-Localization for Large-Scale Applications

CVPR 2022 · Vision-language and multimodal › Human and embodied modeling · approach: Scientific domain vision datasets · 263 citations (OpenAlex)

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 changedWhereMeasuredEffect
Add non-adjacent spatial and orientation-based CosPlace groups (ablation)CosPlace · SF-XL validationRecall@1: 77.1 -> 90.9Helped
Add orientation labels and orientation-based class separation (ablation)CosPlace · SF-XL validationRecall@1: 85.8 -> 90.9Helped
Add orientation group separation (L=2) (ablation)CosPlace · SF-XL validationRecall@1: 88.2 -> 90.9Helped
Add spatial group separation (N=5) (ablation)CosPlace · SF-XL validationRecall@1: 77.1 -> 90.9Helped
Train CosPlace with fewer images (ablation)CosPlace · Pitts250kRecall@1: 90.4 -> 86.0 -> 82.1 -> 73.3Hurt
Train using CosPlace classification instead of contrastive retrieval learningVisual geo-localization · SF-XL test v1R@1: 51.2 -> 64.7Helped
Use a 512-D CosPlace descriptor instead of a 4096-D descriptorVisual geo-localization · SF-XL test v1R@1: 51.2 -> 64.7Helped

Closest papers by meaning

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.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy