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| license: mit |
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| # SelaVPR++ |
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| SelaVPR++ introduces a parameter-, memory-, and time-efficient PEFT method for seamless adaptation of foundation models to visual place recognition, enhancing both parameter and computational efficiency. It also proposes a novel two-stage paradigm using compact binary features for fast candidate retrieval and robust floating-point features for re-ranking, significantly improving retrieval speed. In addition to its high efficiency, this work also outperforms previous state-of-the-art methods on several VPR benchmarks. |
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| **Paper:** [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://arxiv.org/pdf/2502.16601) (Accepted by IEEE T-PAMI 2025) |
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| **GitHub:** [Lu-Feng/SelaVPRplusplus](https://github.com/Lu-Feng/SelaVPRplusplus) |
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| ## Citation |
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| ```bibtex |
| @ARTICLE{selavprpp, |
| author={Lu, Feng and Jin, Tong and Lan, Xiangyuan and Zhang, Lijun and Liu, Yunpeng and Wang, Yaowei and Yuan, Chun}, |
| journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, |
| title={SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition}, |
| year={2025}, |
| volume={}, |
| number={}, |
| pages={1-18}, |
| doi={10.1109/TPAMI.2025.3629287}} |
| ``` |
