# MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery

arXiv: https://arxiv.org/abs/2202.09146 · HTML: https://mlsanity.com/paper/2202.09146/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 66 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 27 of 27)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [End-to-end Learning of Deep Visual Representations for Image Retrieval](https://mlsanity.com/paper/1610.07940/) (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)
- [Aggregating Deep Convolutional Features for Image Retrieval](https://mlsanity.com/paper/1510.07493/) (2015)
- [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)
- [SegMatch: Segment based loop-closure for 3D point clouds](https://mlsanity.com/paper/1609.07720/) (2016)
- [LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics](https://mlsanity.com/paper/1804.05526/) (2018)
- [Attention-based Pyramid Aggregation Network for Visual Place Recognition](https://mlsanity.com/paper/1808.00288/) (2018)
- [Localizing Discriminative Visual Landmarks for Place Recognition](https://mlsanity.com/paper/1904.06635/) (2019)
- [Semantic Reinforced Attention Learning for Visual Place Recognition](https://mlsanity.com/paper/2108.08443/) (2021)
- [Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition](https://mlsanity.com/paper/1811.03529/) (2018)
- [Factors of Transferability for a Generic ConvNet Representation](https://mlsanity.com/paper/1406.5774/) (2014)
- [Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions](https://mlsanity.com/paper/1903.09107/) (2019)
- [Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks](https://mlsanity.com/paper/1709.08810/) (2017)
- [City-Scale Visual Place Recognition with Deep Local Features Based on Multi-Scale Ordered VLAD Pooling](https://mlsanity.com/paper/2009.09255/) (2020)

## Built on by (most cited first, 14 of 14)

- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events](https://mlsanity.com/paper/2211.12244/) (2022)
- [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition](https://mlsanity.com/paper/2209.08723/) (2022)
- [Co-visual pattern augmented generative transformer learning for automobile geo-localization](https://mlsanity.com/paper/2203.09135/) (2022)
- [Leveraging BEV Representation for 360-degree Visual Place Recognition](https://mlsanity.com/paper/2305.13814/) (2023)
- [VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization](https://mlsanity.com/paper/2308.00090/) (2023)
- [Explicit Interaction for Fusion-Based Place Recognition](https://mlsanity.com/paper/2402.17264/) (2024)
- [VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2409.19293/) (2024)
- [TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation](https://mlsanity.com/paper/2404.01587/) (2024)
- [DSFormer: A Dual-Scale Cross-Learning Transformer for Visual Place Recognition](https://mlsanity.com/paper/2507.18444/) (2025)
- [Single-Shot Global Localization via Graph-Theoretic Correspondence Matching](https://mlsanity.com/paper/2306.03641/) (2023)
- [Scalable and Efficient Hierarchical Visual Topological Mapping](https://mlsanity.com/paper/2404.05023/) (2024)
- [Text2Graph VPR: A Text-to-Graph Expert System for Explainable Place Recognition in Changing Environments](https://mlsanity.com/paper/2512.18613/) (2025)
- [GeoSearch: Augmenting Worldwide Geolocalization with Web-Scale Reverse Image Search and Image Matching](https://mlsanity.com/paper/2604.25390/) (2026)

## Abstract (arXiv)

Visual Place Recognition (VPR) is a crucial component of 6-DoF localization, visual SLAM and structure-from-motion pipelines, tasked to generate an initial list of place match hypotheses by matching global place descriptors. However, commonly-used CNN-based methods either process multiple image resolutions after training or use a single resolution and limit multi-scale feature extraction to the last convolutional layer during training. In this paper, we augment NetVLAD representation learning with low-resolution image pyramid encoding which leads to richer place representations. The resultant multi-resolution feature pyramid can be conveniently aggregated through VLAD into a single compact representation, avoiding the need for concatenation or summation of multiple patches in recent multi-scale approaches. Furthermore, we show that the underlying learnt feature tensor can be combined with existing multi-scale approaches to improve their baseline performance. Evaluation on 15 viewpoint-varying and viewpoint-consistent benchmarking datasets confirm that the proposed MultiRes-NetVLAD leads to state-of-the-art Recall@N performance for global descriptor based retrieval, compared against 11 existing techniques. Source code is publicly available at https://github.com/Ahmedest61/MultiRes-NetVLAD.