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SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition by Lu, Feng; Jin, Tong; Lan, Xiangyuan; Zhang, Lijun; Liu, Yunpeng; Wang, Yaowei; Yuan, Chun is a scholarly article available to read on EtoBox.

What is SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition about?

Recent studies show that the visual place recognition (VPR) method using pre-trained visual foundation models can achieve promising performance. In our previous work, we propose a novel method to realize seamless adaptation of foundation models to VPR (SelaVPR). This method can produce both global and local features that focus on discriminative landmarks to recognize places for two-stage VPR by a parameter-efficient adaptation approach. Although SelaVPR has achieved competitive results, we argue that the previous adaptation is inefficient in training time and GPU memory usage, and the re-ranking paradigm is also costly in retrieval latency and storage usage. In pursuit of higher efficiency and better performance, we propose an extension of the SelaVPR, called SelaVPR++. Concretely, we first design a parameter-, time-, and memory-efficient adaptation method that uses lightweight multi-scale convolution (MultiConv) adapters to refine intermediate features from the frozen foundation backbone. This adaptation method does not back-propagate gradients through the backbone during training, and the MultiConv adapter facilitates feature interactions along the spatial axes and introduces pro

Author

Lu, Feng; Jin, Tong; Lan, Xiangyuan; Zhang, Lijun; Liu, Yunpeng; Wang, Yaowei; Yuan, Chun

Published

2025

Language

EN