## 1 Introduction  
Visual place recognition (VPR) is a key research area in computer vision and robotics that aims to localize an agent by matching query images against place-tagged reference datasets [1, 2]. It plays a crucial role in applications such as mobile robotic localization [3], augmented reality [4], and autonomous navigation [5], where accurate place recognition is essential for state estimation in complex environments. Despite substantial advancements, VPR remains a challenging task due to factors such as appearance variations [6, 7], viewpoint discrepancies [8, 9], and perceptual aliasing [10, 11], where visually similar locations lead to incorrect matches. Typically, VPR is formulated as an image retrieval problem, where a query image is represented as a compact feature descriptor and compared against a database of place-tagged reference images to retrieve the closest match. The process of generating a global descriptor generally involves two key steps: local feature extraction and global descriptor aggregation. Conventional VPR methods commonly rely on combinations of handcrafted local features with global descriptors to generate global image representations. Examples include SURF [12] with Vector of Locally Aggregated Descriptors (VLAD) [13], and ORB [14] with Bag of Words (BoW) [15]. However, these approaches fail to adequately preserve geometric and structural information, making it challenging to handle lighting and appearance variations. Targeting these limitations, advanced deep learning models are leveraged to enhance VPR performance by improving both local feature extraction and global descriptor aggregation. In terms of local feature extraction, early approaches replaced hand-crafted features with Convolutional Neural Networks (CNNs) [16], demonstrating improved robustness and scalability. In contrast, Vision Transformer (ViT) [17] leverages a self-attention mechanism to effectively capture global dependencies in images, thus excelling at understanding the overall scene structure and long-range relationships. In the context of VPR, ViT precisely models relationships between regions, such as the positions of buildings or the interaction between landmarks and backgrounds, thereby generating unique global descriptors. This makes ViT well suited for tasks demanding holistic scene comprehension. Consequently, visual foundation models (VFMs), such as AnyLoc [18] and CricaVPR [19], which are based on ViT, have gained popularity due to their strong generalization capabilities. Regarding global descriptor aggregation, NetVLAD [20] stands as a milestone, introducing a differentiable modification of the VLAD layer within a CNN for end-to-end optimization. Additionally, MixVPR [21] enhances feature aggregation by incorporating a multilayer perceptron (MLP), thus improving feature expressiveness. In contrast, generalized mean pooling (GeM) [22] prioritizes efficiency, utilizing a simple yet effective pooling mechanism for global descriptor generation. More recently, a promising development is the Sinkhorn Algorithm for Locally Aggregated Descriptors (SALAD) [23], which formulates feature aggregation as an optimal transport problem. By selectively discarding uninformative local features, SALAD improves the discriminative power of global descriptors. However, its reliance on fine-tuning DiNOv2, a VFM, increases computational costs, as it requires unfreezing and retraining the final layers of the model. Additionally, SALAD processes each query image independently, without leveraging contextual relationships between images in the database--an aspect that could further enhance recognition robustness. To address these limitations, we propose CriSALAD, a novel visual place recognition framework that integrates cross-image information with optimal transport-based feature aggregation. Inspired by CricaVPR [19], we employ parameter-efficient transfer learning, incorporating a lightweight depthwise separable convolutional adapter into the frozen pretrained backbone.