## 2 Related Works  
### Visual Place Recognition  
Following the milestone work NetVLAD [20], which first enabled end-to-end training for VPR by integrating CNN-based feature extraction with a differentiable VLAD layer, many subsequent methods have been proposed. In image-based place recognition, Patch-NetVLAD [24] derives patch-level features and combines local and global descriptor methods. Ref. [25] proposed a hardness-aware metric learning method with cluster-guided attention. VIPeR [26] balances performance in a single environment with generality across multiple environments. In the domain of point cloud-based place recognition, Indoor DH3D from FD-SLAM [27] introduces color into 3D point clouds, CGiS-Net [10] identifies indoor places from color point clouds by aggregating low-level color and geometric features with high-level implicit semantic features, and AEGIS-Net [11] uses a self-attention module to select the most important local features.  
In particular, to address the issue of perceptual aliasing, [PERSON] et al. proposed a variational autoencoder that effectively extracts condition-invariant features by leveraging probabilistic modeling to separate environment-dependent and environment-independent features [28]. Meanwhile, [PERSON] et al. demonstrated that the integration of attention mechanisms can effectively identify salient regions [6]. [PERSON] et al. proposed the use of global features from scan-context descriptors and dictionary-based coding [29], enabling the model to handle challenging perceptual aliasing scenarios. Focusing on the viewpoint variant problem, [PERSON] et al. introduced a coarse-to-fine hierarchical VPR pipeline that integrates semantic aggregation and scene understanding [30].  
Apart from using a NetVLAD [20] layer for global aggregation, there are many other popular aggregation methods, such as pooling layer-based approaches [22], learning global aggregation [21], and optimal transport aggregation [23]. Moreover, instead of formulating VPR as a retrieval problem, CosPlace [31] and EigenPlaces [9] innovatively treat the VPR task as a classification problem and have achieved impressive VPR performance, with reduced GPU memory consumption and smaller descriptor size.  
Considering the powerful feature extraction capabilities of ViT, many methods have incorporated ViT into the VPR task. AnyLoc [18] directly utilizes a pre-trained DiNOv2 [32] model as a feature extractor, achieving promising results. Following this, many other methods have been proposed to fine-tune the pre-trained DiNOv2 model to not only leverage its general feature extraction ability but also fit the model better for the VPR task. In particular, DiNO-MIX [33] and DiNO-SALAD [23] use a straightforward approach, fine-tuning the last few blocks of the DiNOv2 model. On the other hand, CricaVPR [19] and SelaVPR [34] use parameter-efficient fine-tuning, also known as parameter-efficient transfer learning, to freeze the weights of DiNOv2, utilizing different adapter architectures to enable fine-tuning. Our proposed CriSALAD model also utilizes the parameter-efficient fine-tuning approach and achieves better performance with fewer fine-tuning parameters.