The paper presents FedVPR, a novel federated learning framework for Visual Place Recognition (VPR) tasks. VPR aims to estimate the location of an image by treating it as a retrieval problem, where a database of geo-tagged images is used to find the most similar matches.
The key contributions are:
The paper first establishes centralized baselines for VPR, exploring different model architectures and pooling layers. It then analyzes the performance of the vanilla FedAvg algorithm across the proposed federated datasets, highlighting the impact of data quantity skewness and the importance of addressing it through techniques like FedVC.
Furthermore, the paper investigates the effect of heterogeneous data augmentation on federated training, demonstrating the severe performance degradation caused by client-specific color jitter. It also analyzes the impact of local mining, showing that a moderate geographical scope can be beneficial for VPR, in contrast to the traditional assumption that geographical diversity is essential.
Overall, the work introduces FedVPR as a new and challenging task for the federated learning research community, paving the way for future advancements in distributed visual place recognition.
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by Matt... alle arxiv.org 04-23-2024
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