Temporal Attention for Cross-View Sequential Image Localization
<|reference_start|>Temporal Attention for Cross-View Sequential Image Localization: This paper introduces a novel approach to enhancing cross-view localization, focusing on the fine-grained, sequential localization of street-view images within a single known satellite image patch, a significant departure from traditional one-to-one image retrieval methods. By expanding to sequential image fine-grained localization, our model, equipped with a novel Temporal Attention Module (TAM), leverages contextual information to significantly improve sequential image localization accuracy. Our method shows substantial reductions in both mean and median localization errors on the Cross-View Image Sequence (CVIS) dataset, outperforming current state-of-the-art single-image localization techniques. Additionally, by adapting the KITTI-CVL dataset into sequential image sets, we not only offer a more realistic dataset for future research but also demonstrate our model's robust generalization capabilities across varying times and areas, evidenced by a 75.3% reduction in mean distance error in cross-view sequential image localization.<|reference_end|>
@article{yuan2024temporal,
    title={Temporal Attention for Cross-View Sequential Image Localization},
    author={Dong Yuan, Frederic Maire, Feras Dayoub},
    journal={arXiv preprint arXiv:2408.15569},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15569},
    primaryClass={cs.CV}
}