# Learning Sequential Descriptors for Sequence-based Visual Place Recognition

arXiv: https://arxiv.org/abs/2207.03868 · HTML: https://mlsanity.com/paper/2207.03868/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Escaping the Big Data Paradigm with Compact Transformers](https://mlsanity.com/paper/2104.05704/) (2021)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://mlsanity.com/paper/1804.03492/) (2018)
- [Training Vision Transformers for Image Retrieval](https://mlsanity.com/paper/2102.05644/) (2021)
- [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021)
- [DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization](https://mlsanity.com/paper/2007.09217/) (2020)
- [Delta Descriptors: Change-Based Place Representation for Robust Visual Localization](https://mlsanity.com/paper/2006.05700/) (2020)
- [Condition-Invariant Multi-View Place Recognition](https://mlsanity.com/paper/1902.09516/) (2019)
- [3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification](https://mlsanity.com/paper/1812.10222/) (2018)
- [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021)

## Built on by (most cited first, 25 of 25)

- [EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition](https://mlsanity.com/paper/2308.10832/) (2023)
- [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition](https://mlsanity.com/paper/2403.19787/) (2024)
- [MeshVPR: Citywide Visual Place Recognition Using 3D Meshes](https://mlsanity.com/paper/2406.02776/) (2024)
- [Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates](https://mlsanity.com/paper/2407.08162/) (2024)
- [UniPR-3D: Towards Universal Visual Place Recognition with Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2512.21078/) (2025)
- [GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2410.00299/) (2024)
- [Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition](https://mlsanity.com/paper/2305.11467/) (2023)
- [Improving Visual Place Recognition with Sequence-Matching Receptiveness Prediction](https://mlsanity.com/paper/2503.06840/) (2025)
- [VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2409.19293/) (2024)
- [To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition](https://mlsanity.com/paper/2504.06116/) (2025)
- [Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition](https://mlsanity.com/paper/2307.08417/) (2023)
- [Collaborative Visual Place Recognition through Federated Learning](https://mlsanity.com/paper/2404.13324/) (2024)
- [Flexible and Efficient Spatio-Temporal Transformer for Sequential Visual Place Recognition](https://mlsanity.com/paper/2510.04282/) (2025)
- [VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale](https://mlsanity.com/paper/2604.12159/) (2026)
- [VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition](https://mlsanity.com/paper/2403.09025/) (2024)
- [SciceVPR: Stable Cross-Image Correlation Enhanced Model for Visual Place Recognition](https://mlsanity.com/paper/2502.20676/) (2025)
- [Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition](https://mlsanity.com/paper/2510.17739/) (2025)
- [Text2Graph VPR: A Text-to-Graph Expert System for Explainable Place Recognition in Changing Environments](https://mlsanity.com/paper/2512.18613/) (2025)
- [DisPlace: Discriminative Place Projections for Multi-Reference Visual Place Recognition](https://mlsanity.com/paper/2605.30769/) (2026)
- [Route-MHT: Multimodal Transformer Guardrails for Thermal Visual Place Recognition](https://mlsanity.com/paper/2607.04745/) (2026)
- [PixelLoop: Shortcut Topological Navigation with Pixel-Level Loops](https://mlsanity.com/paper/2607.12811/) (2026)
- [FoundYou: A Unified Model for Personalized Segmentation and Retrieval](https://mlsanity.com/paper/2608.29917/) (2026)
- [TRAIL: Trajectory-Aware Visual Place Recognition against Unordered Databases](https://mlsanity.com/paper/2609.07373/) (2026)

## Abstract (arXiv)

In robotics, Visual Place Recognition is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This task requires robust, scalable, and efficient techniques for real applications. This work proposes a detailed taxonomy of techniques using sequential descriptors, highlighting different mechanism to fuse the information from the individual images. This categorization is supported by a complete benchmark of experimental results that provides evidence on the strengths and weaknesses of these different architectural choices. In comparison to existing sequential descriptors methods, we further investigate the viability of Transformers instead of CNN backbones, and we propose a new ad-hoc sequence-level aggregator called SeqVLAD, which outperforms prior state of the art on different datasets. The code is available at https://github.com/vandal-vpr/vg-transformers.