OraP: Oracle-Inspired Early Token Pruning for Efficient Visual Place Recognition

Visual place recognition (VPR) has recently achieved remarkable performance with the emergence of powerful foundation models. Nonetheless, their high computational cost remains a major obstacle for efficient deployment. Token pruning offers a promising route to faster inference, yet aggressive early pruning, where the computational benefit is greatest, often causes substantial performance degradation. In this work, we uncover that token rankings induced by the final-block attention provide a highly reliable pruning signal. An oracle study further reveals that these rankings can guide early pruning with near-lossless performance. However, obtaining the oracle rankings requires an additional forward pass and therefore provides no practical acceleration. To bridge this gap, we propose OraP, an oracle-inspired early token pruning framework that anticipates late-emerging token rankings from early representations. Instead of reproducing exact attention values or distributions, OraP preserves only selection-relevant ranking relationships, enabling reliable early pruning in a single forward pass. The proposed OraP can be readily applied to accelerate various representative transformer-based VPR models. Experimental results show that OraP substantially outperforms existing token reduction methods under the same early pruning setting. By removing 50% of the patch tokens within the first transformer block, OraP reduces FLOPs by 48.9% and achieves 2.05× inference throughput with an average absolute R@1 degradation of only 0.8% across twelve benchmark datasets. On an NVIDIA Jetson Xavier NX, OraP achieves a 1.68× speedup in single-query inference, supporting its practicality for edge deployment. Code and models will be released.