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Visual Place Recognition On Tokyo247

Metrics

Recall@1

Results

Performance results of various models on this benchmark

Paper Title
Pair-VPR-p100Pair-VPR: Place-Aware Pre-training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers
EffoVPR98.7EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition
Pair-VPR-s98.1Pair-VPR: Place-Aware Pre-training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers
BoQ98.1BoQ: A Place is Worth a Bag of Learnable Queries
SelaVPR94.0Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
EigenPlaces93EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
ProGEO88.6ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization
Patch-NetVLAD86Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
CosPlace82.2Rethinking Visual Geo-localization for Large-Scale Applications
GCL [trained only on MSLS]69.84Generalized Contrastive Optimization of Siamese Networks for Place Recognition
CosPlace (ResNet-101 2048-D)-Rethinking Visual Geo-localization for Large-Scale Applications
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Visual Place Recognition On Tokyo247 | SOTA | HyperAI