# Online Visual Place Recognition via Saliency Re-identification

arXiv: https://arxiv.org/abs/2007.14549 · HTML: https://mlsanity.com/paper/2007.14549/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation](https://mlsanity.com/paper/2105.07645/) (2021)
- [Image-based Geo-localization for Robotics: Are Black-box Vision-Language Models there yet?](https://mlsanity.com/paper/2501.16947/) (2025)
- [SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words](https://mlsanity.com/paper/2110.11491/) (2021)
- [Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution](https://mlsanity.com/paper/2202.03126/) (2022)
- [VLM-Guided Visual Place Recognition for Planet-Scale Geo-Localization](https://mlsanity.com/paper/2507.17455/) (2025)
- [Assessing the Geolocation Capabilities, Limitations and Societal Risks of Generative Vision-Language Models](https://mlsanity.com/paper/2508.19967/) (2025)
- [Saliency for free: Saliency prediction as a side-effect of object recognition](https://mlsanity.com/paper/2107.09628/) (2021)
- [Spectrum-inspired Low-light Image Translation for Saliency Detection](https://mlsanity.com/paper/2303.10145/) (2023)
- [LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes](https://mlsanity.com/paper/2304.03872/) (2023)

## Abstract (arXiv)

As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency re-identification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.