Anchored Rank-Adversarial Fine-Tuning Pretrained Visual Place Recognition Models

Summary

Foundation-model visual place recognition models such as SuperVLAD and BoQ keep at most 0.2% top-1 recall under a white-box ranking attack at a 1/255 budget. The proposed anchored rank-adversarial fine-tuning (ARAFT) raises robust R@1 from zero to 47.3 on SuperVLAD and 55.3 on BoQ within five clean R@1 points, where plain adversarial training yields no admissible checkpoint. ARAFT also transfers from SuperVLAD to BoQ without re-tuning and holds against stronger optimisers.

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