# Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition

arXiv: https://arxiv.org/abs/2312.09028 · HTML: https://mlsanity.com/paper/2312.09028/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Post-training Quantization on Diffusion Models](https://mlsanity.com/paper/2211.15736/) (2022)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Quantized Convolutional Neural Networks for Mobile Devices](https://mlsanity.com/paper/1512.06473/) (2015)
- [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023)
- [VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change](https://mlsanity.com/paper/2005.08135/) (2020)
- [GSV-Cities: Toward Appropriate Supervised Visual Place Recognition](https://mlsanity.com/paper/2210.10239/) (2022)
- [Deep Visual Geo-localization Benchmark](https://mlsanity.com/paper/2204.03444/) (2022)
- [NVIDIA Tensor Core Programmability, Performance & Precision](https://mlsanity.com/paper/1803.04014/) (2018)
- [Aggregating Deep Convolutional Features for Image Retrieval](https://mlsanity.com/paper/1510.07493/) (2015)
- [Visual Instance Retrieval with Deep Convolutional Networks](https://mlsanity.com/paper/1412.6574/) (2014)
- [Single-View Place Recognition under Seasonal Changes](https://mlsanity.com/paper/1808.06516/) (2018)
- [Transform Quantization for CNN (Convolutional Neural Network) Compression](https://mlsanity.com/paper/2009.01174/) (2020)
- [Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2010.00716/) (2020)
- [Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics](https://mlsanity.com/paper/2104.11849/) (2021)
- [Highly-Efficient Binary Neural Networks for Visual Place Recognition](https://mlsanity.com/paper/2202.12375/) (2022)

## Built on by (most cited first, 11 of 11)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition](https://mlsanity.com/paper/2502.16601/) (2025)
- [SuperPlace: The Renaissance of Classical Feature Aggregation for Visual Place Recognition in the Era of Foundation Models](https://mlsanity.com/paper/2506.13073/) (2025)
- [Structured Pruning for Efficient Visual Place Recognition](https://mlsanity.com/paper/2409.07834/) (2024)
- [TextInPlace: Indoor Visual Place Recognition in Repetitive Structures with Scene Text Spotting and Verification](https://mlsanity.com/paper/2503.06501/) (2025)
- [TeTRA-VPR: A Ternary Transformer Approach for Compact Visual Place Recognition](https://mlsanity.com/paper/2503.02511/) (2025)
- [TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition](https://mlsanity.com/paper/2505.16447/) (2025)
- [Learning 1-Bit LiDAR-based Localization with Auxiliary Objective](https://mlsanity.com/paper/2606.27729/) (2026)
- [Are All Tokens Necessary for Visual Place Recognition? An Empirical Study of Token Reduction for Efficient Inference](https://mlsanity.com/paper/2607.15563/) (2026)
- [Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics](https://mlsanity.com/paper/2607.18540/) (2026)
- [Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks](https://mlsanity.com/paper/2610.16022/) (2026)

## Abstract (arXiv)

Visual Place Recognition (VPR) is a critical task for performing global re-localization in visual perception systems. It requires the ability to accurately recognize a previously visited location under variations such as illumination, occlusion, appearance and viewpoint. In the case of robotic systems and augmented reality, the target devices for deployment are battery powered edge devices. Therefore whilst the accuracy of VPR methods is important so too is memory consumption and latency. Recently new works have focused on the recall@1 metric as a performance measure with limited focus on resource utilization. This has resulted in methods that use deep learning models too large to deploy on low powered edge devices. We hypothesize that these large models are highly over-parameterized and can be optimized to satisfy the constraints of a low powered embedded system whilst maintaining high recall performance. Our work studies the impact of compact convolutional network architecture design in combination with full-precision and mixed-precision post-training quantization on VPR performance. Importantly we not only measure performance via the recall@1 score but also measure memory consumption and latency. We characterize the design implications on memory, latency and recall scores and provide a number of design recommendations for VPR systems under these resource limitations.