{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pair-vpr-place-aware-pre-training-and","title":"Pair-VPR: Place-Aware Pre-training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers","arxiv_id":"2410.06614","date":"2024-10-09","proceeding":null,"authors":["Stephen Hausler","Peyman Moghadam"],"abstract":"In this work we propose a novel joint training method for Visual Place Recognition (VPR), which simultaneously learns a global descriptor and a pair classifier for re-ranking. The pair classifier can predict whether a given pair of images are from the same place or not. The network only comprises Vision Transformer components for both the encoder and the pair classifier, and both components are trained using their respective class tokens. In existing VPR methods, typically the network is initialized using pre-trained weights from a generic image dataset such as ImageNet. In this work we propose an alternative pre-training strategy, by using Siamese Masked Image Modelling as a pre-training task. We propose a Place-aware image sampling procedure from a collection of large VPR datasets for pre-training our model, to learn visual features tuned specifically for VPR. By re-using the Mask Image Modelling encoder and decoder weights in the second stage of training, Pair-VPR can achieve state-of-the-art VPR performance across five benchmark datasets with a ViT-B encoder, along with further improvements in localization recall with larger encoders. The Pair-VPR website is: https://csiro-robotics.github.io/Pair-VPR.","url_abs":"https://arxiv.org/abs/2410.06614v2","url_pdf":"https://arxiv.org/pdf/2410.06614v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pair-vpr-place-aware-pre-training-and","repo_url":"https://github.com/csiro-robotics/Pair-VPR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":"visual-place-recognition","task_name":"Visual Place Recognition"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-place-recognition-on-mapillary-test","task":"Visual Place Recognition","dataset":"Mapillary test","model":"Pair-VPR-p","rank_in_archive_order":2,"of":12,"metrics":{"Recall@1":"81.7","Recall@10":"91.3","Recall@5":"90.2"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-mapillary-test","task":"Visual Place Recognition","dataset":"Mapillary test","model":"Pair-VPR-s","rank_in_archive_order":6,"of":12,"metrics":{"Recall@1":"79.0","Recall@10":"88.3","Recall@5":"86.9"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-mapillary-val","task":"Visual Place Recognition","dataset":"Mapillary val","model":"Pair-VPR-p","rank_in_archive_order":2,"of":18,"metrics":{"Recall@1":"95.4","Recall@10":"97.7","Recall@5":"97.3"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-mapillary-val","task":"Visual Place Recognition","dataset":"Mapillary val","model":"Pair-VPR-s","rank_in_archive_order":4,"of":18,"metrics":{"Recall@1":"93.7","Recall@10":"97.3","Recall@5":"97.2"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-pittsburgh-30k","task":"Visual Place Recognition","dataset":"Pittsburgh-30k-test","model":"Pair-VPR-p","rank_in_archive_order":1,"of":22,"metrics":{"Recall@1":"95.4","Recall@10":"98.0","Recall@5":"97.5"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-pittsburgh-30k","task":"Visual Place Recognition","dataset":"Pittsburgh-30k-test","model":"Pair-VPR-s","rank_in_archive_order":2,"of":22,"metrics":{"Recall@1":"94.7","Recall@10":"97.8","Recall@5":"97.2"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-tokyo247","task":"Visual Place Recognition","dataset":"Tokyo247","model":"Pair-VPR-p","rank_in_archive_order":1,"of":14,"metrics":{"Recall@1":"100","Recall@10":"100","Recall@5":"100"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-tokyo247","task":"Visual Place Recognition","dataset":"Tokyo247","model":"Pair-VPR-s","rank_in_archive_order":5,"of":14,"metrics":{"Recall@1":"98.1","Recall@10":"98.7","Recall@5":"98.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.06614","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}