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Found 12 papers, 11 papers with code
Collaborative Visual Place Recognition through Federated Learning
Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem.
JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition
As a mitigation to this problem, we propose a novel Joint Image and Sequence Training protocol (JIST) that leverages large uncurated sets of images through a multi-task learning framework.
EarthLoc: Astronaut Photography Localization by Indexing Earth from Space
Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response.
EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
Visual Place Recognition is a task that aims to predict the place of an image (called query) based solely on its visual features.
Ranked #1 on
Visual Place Recognition
on SF-XL test v1
Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition
Our method, Divide&Classify (D&C), enjoys the fast inference of classification solutions and an accuracy competitive with retrieval methods on the fine-grained, city-wide setting.
Are Local Features All You Need for Cross-Domain Visual Place Recognition?
Despite recent advances, recognizing the same place when the query comes from a significantly different distribution is still a major hurdle for state of the art retrieval methods.
Divide&Classify: Fine-Grained Classification for City-Wide Visual Geo-Localization
In this paper we investigate whether we can effectively approach this task as a classification problem, thus bypassing the need for a similarity search.
Learning Sequential Descriptors for Sequence-based Visual Place Recognition
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.
Deep Visual Geo-localization Benchmark
In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline.
Rethinking Visual Geo-localization for Large-Scale Applications
Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations.
Ranked #1 on
Visual Place Recognition
on SF-XL test v2
Learning Semantics for Visual Place Recognition through Multi-Scale Attention
In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery.
Viewpoint Invariant Dense Matching for Visual Geolocalization
Dense local features matching is robust against changes in illumination and occlusions, but not against viewpoint shifts which are a fundamental aspect of geolocalization.
