# Distillation Improves Visual Place Recognition for Low Quality Images

arXiv: https://arxiv.org/abs/2310.06906 · HTML: https://mlsanity.com/paper/2310.06906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 50 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 30 of 33)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Similarity-Preserving Knowledge Distillation](https://mlsanity.com/paper/1907.09682/) (2019)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting](https://mlsanity.com/paper/2307.12348/) (2023)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring](https://mlsanity.com/paper/2211.12250/) (2022)
- [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)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)
- [Fast and Accurate Single Image Super-Resolution via Information Distillation Network](https://mlsanity.com/paper/1803.09454/) (2018)
- [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)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation](https://mlsanity.com/paper/2202.03680/) (2022)
- [Scalable Place Recognition Under Appearance Change for Autonomous Driving](https://mlsanity.com/paper/1908.00178/) (2019)
- [Large-scale Localization Datasets in Crowded Indoor Spaces](https://mlsanity.com/paper/2105.08941/) (2021)
- [Localizing Discriminative Visual Landmarks for Place Recognition](https://mlsanity.com/paper/1904.06635/) (2019)
- [Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2010.00716/) (2020)
- [StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2212.00937/) (2022)
- [A Multi-Domain Feature Learning Method for Visual Place Recognition](https://mlsanity.com/paper/1902.10058/) (2019)
- [An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments](https://mlsanity.com/paper/2109.10986/) (2021)
- [NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences](https://mlsanity.com/paper/2110.09004/) (2021)
- [Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations](https://mlsanity.com/paper/2001.08434/) (2020)
- [Unsupervised Quality Prediction for Improved Single-Frame and Weighted Sequential Visual Place Recognition](https://mlsanity.com/paper/2307.01464/) (2023)
- [UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision](https://mlsanity.com/paper/2209.11336/) (2022)
- [Dominating Set Database Selection for Visual Place Recognition](https://mlsanity.com/paper/2303.05123/) (2023)
- [Visual Place Recognition with Low-Resolution Images](https://mlsanity.com/paper/2305.05776/) (2023)

## Abstract (arXiv)

Real-time visual localization often utilizes online computing, for which query images or videos are transmitted to remote servers for visual place recognition (VPR). However, limited network bandwidth necessitates image-quality reduction and thus the degradation of global image descriptors, reducing VPR accuracy. We address this issue at the descriptor extraction level with a knowledge-distillation methodology that learns feature representations from high-quality images to extract more discriminative descriptors from low-quality images. Our approach includes the Inter-channel Correlation Knowledge Distillation (ICKD) loss, Mean Squared Error (MSE) loss, and Triplet loss. We validate the proposed losses on multiple VPR methods and datasets subjected to JPEG compression, resolution reduction, and video quantization. We obtain significant improvements in VPR recall rates under all three tested modalities of lowered image quality. Furthermore, we fill a gap in VPR literature on video-based data and its influence on VPR performance. This work contributes to more reliable place recognition in resource-constrained environments.