Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+# SelaVPR++
+
+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.
+
+**Paper:** [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://arxiv.org/pdf/2502.16601) (Accepted by IEEE T-PAMI 2025)
+
+**GitHub:** [Lu-Feng/SelaVPRplusplus](https://github.com/Lu-Feng/SelaVPRplusplus)
+
+## Citation
+
+```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}}
+```