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R2Former: Unified Retrieval and Reranking Transformer for Place Recognition

CVPR 2023 · Architectures and efficiency › Attention and transformers · approach: Efficient image transformers · 111 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use R2Former instead of TransVPRVPR retrieval and reranking · MSLS ValR@1: 86.8 -> 89.7Helped
Use R2Former instead of TransVPRVPR retrieval and reranking · MSLS ChallengeR@1: 63.9 -> 73.0Helped
Use R2Former instead of TransVPRVPR retrieval and reranking · Pitts30kR@1: 89.0 -> 91.1Helped
Use R2Former instead of TransVPRVPR retrieval and reranking · Tokyo 24/7R@1: 79.0 -> 88.6Helped
Add the learned R2Former rerankerR2Former global retrieval on MSLS Val · MSLS ValR@1: 79.3 -> 89.7Helped
Add global hard-negative mining and joint finetuning after reranker training (ablation)R2Former training · MSLS ValR@1: 86.6 -> 89.7Helped
Train retrieval and reranking end-to-end from the beginning (ablation)R2Former training · MSLS ValR@1: 79.3 -> 87.3Helped
Add positional embedding (ablation)R2Former reranking module · MSLS ValR@1: 86.4 -> 86.6No clear effect
Add Transformer Block-1 (ablation)R2Former reranking module · MSLS ValR@1: 85.5 -> 86.6Helped
Add Transformer Block-2 (ablation)R2Former reranking module · MSLS ValR@1: 81.9 -> 86.6Helped
Use Top-5 feature pairs instead of Top-1 (ablation)R2Former reranking module · MSLS ValR@1: 86.6 -> 86.6No clear effect
Use attention-based feature selection (ablation)R2Former reranking module · MSLS ValR@1: 83.6 -> 86.6Helped

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