# A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition

arXiv: https://arxiv.org/abs/2107.02440 · HTML: https://mlsanity.com/paper/2107.02440/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 21 of 21)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [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)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Deep Image Retrieval: Learning global representations for image search](https://mlsanity.com/paper/1604.01325/) (2016)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [Deep Learning Features at Scale for Visual Place Recognition](https://mlsanity.com/paper/1701.05105/) (2017)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes](https://mlsanity.com/paper/1811.03032/) (2018)
- [X-View: Graph-Based Semantic Multi-View Localization](https://mlsanity.com/paper/1709.09905/) (2017)
- [Single-View Place Recognition under Seasonal Changes](https://mlsanity.com/paper/1808.06516/) (2018)
- [LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics](https://mlsanity.com/paper/1804.05526/) (2018)
- [Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization](https://mlsanity.com/paper/1908.06387/) (2019)
- [Delta Descriptors: Change-Based Place Representation for Robust Visual Localization](https://mlsanity.com/paper/2006.05700/) (2020)
- [Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks](https://mlsanity.com/paper/1709.08810/) (2017)

## Built on by (most cited first, 15 of 15)

- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [UFM: A Simple Path towards Unified Dense Correspondence with Flow](https://mlsanity.com/paper/2506.09278/) (2025)
- [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023)
- [Revisit Anything: Visual Place Recognition via Image Segment Retrieval](https://mlsanity.com/paper/2409.18049/) (2024)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals](https://mlsanity.com/paper/2509.08699/) (2025)
- [AirObject: A Temporally Evolving Graph Embedding for Object Identification](https://mlsanity.com/paper/2111.15150/) (2021)
- [Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems](https://mlsanity.com/paper/2206.13883/) (2022)
- [Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition](https://mlsanity.com/paper/2202.05738/) (2022)
- [VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2409.19293/) (2024)
- [EchoVPR: Echo State Networks for Visual Place Recognition](https://mlsanity.com/paper/2110.05572/) (2021)
- [ReF -- Rotation Equivariant Features for Local Feature Matching](https://mlsanity.com/paper/2203.05206/) (2022)
- [AirLoc: Object-based Indoor Relocalization](https://mlsanity.com/paper/2304.00954/) (2023)
- [On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features](https://mlsanity.com/paper/2410.18573/) (2024)
- [Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition](https://mlsanity.com/paper/2201.03212/) (2022)

## Abstract (arXiv)

Visual Place Recognition (VPR) approaches have typically attempted to match places by identifying visual cues, image regions or landmarks that have high ``utility'' in identifying a specific place. But this concept of utility is not singular - rather it can take a range of forms. In this paper, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues `specific' to an environment, and to a particular place. We employ contrastive learning principles to estimate both the environment- and place-specific utility of Vector of Locally Aggregated Descriptors (VLAD) clusters in an unsupervised manner, which is then used to guide local feature matching through keypoint selection. By combining these two utility measures, our approach achieves state-of-the-art performance on three challenging benchmark datasets, while simultaneously reducing the required storage and compute time. We provide further analysis demonstrating that unsupervised cluster selection results in semantically meaningful results, that finer grained categorization often has higher utility for VPR than high level semantic categorization (e.g. building, road), and characterise how these two utility measures vary across different places and environments. Source code is made publicly available at https://github.com/Nik-V9/HEAPUtil.