# Context-Based Visual-Language Place Recognition

arXiv: https://arxiv.org/abs/2410.19341 · HTML: https://mlsanity.com/paper/2410.19341/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Long-term Visual Localization using Semantically Segmented Images](https://mlsanity.com/paper/1801.05269/) (2018)
- [TextSLAM: Visual SLAM with Planar Text Features](https://mlsanity.com/paper/1912.05002/) (2019)
- [FM-Loc: Using Foundation Models for Improved Vision-based Localization](https://mlsanity.com/paper/2304.07058/) (2023)
- [TextSLAM: Visual SLAM with Semantic Planar Text Features](https://mlsanity.com/paper/2305.10029/) (2023)
- [Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs](https://mlsanity.com/paper/2311.12245/) (2023)
- [No More Potentially Dynamic Objects: Static Point Cloud Map Generation based on 3D Object Detection and Ground Projection](https://mlsanity.com/paper/2407.01073/) (2024)

## Built on by (most cited first, 2 of 2)

- [GOTPR: General Outdoor Text-based Place Recognition Using Scene Graph Retrieval with OpenStreetMap](https://mlsanity.com/paper/2501.08575/) (2025)
- [From Open Waters to Enclosed Cabins: ProteusVPR for Cross-Scene Visual Place Recognition in Maritime Perception and Cabin Inspection](https://mlsanity.com/paper/2606.24234/) (2026)

## Abstract (arXiv)

In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite significant progress in recent years, place recognition based on low-level visual features is challenging when there are changes in scene appearance. To address this, end-to-end training approaches have been proposed to overcome the limitations of hand-crafted features. However, these approaches still fail under drastic changes and require large amounts of labeled data to train models, presenting a significant limitation. Methods that leverage high-level semantic information, such as objects or categories, have been proposed to handle variations in appearance. In this paper, we introduce a novel VPR approach that remains robust to scene changes and does not require additional training. Our method constructs semantic image descriptors by extracting pixel-level embeddings using a zero-shot, language-driven semantic segmentation model. We validate our approach in challenging place recognition scenarios using real-world public dataset. The experiments demonstrate that our method outperforms non-learned image representation techniques and off-the-shelf convolutional neural network (CNN) descriptors. Our code is available at https: //github.com/woo-soojin/context-based-vlpr.