Deep Visual Geo-localization Benchmark
- URL: http://arxiv.org/abs/2204.03444v2
- Date: Fri, 9 Jun 2023 10:18:20 GMT
- Title: Deep Visual Geo-localization Benchmark
- Authors: Gabriele Berton, Riccardo Mereu, Gabriele Trivigno, Carlo Masone,
Gabriela Csurka, Torsten Sattler, Barbara Caputo
- Abstract summary: We propose a new open-source benchmarking framework for Visual Geo-localization (VG)
This framework allows to build, train, and test a wide range of commonly used architectures.
Code and trained models are available at https://deep-vg-bench.herokuapp.com/.
- Score: 42.675402470265674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. The purpose of this framework is
twofold: i) gaining insights into how different components and design choices
in a VG pipeline impact the final results, both in terms of performance
(recall@N metric) and system requirements (such as execution time and memory
consumption); ii) establish a systematic evaluation protocol for comparing
different methods. Using the proposed framework, we perform a large suite of
experiments which provide criteria for choosing backbone, aggregation and
negative mining depending on the use-case and requirements. We also assess the
impact of engineering techniques like pre/post-processing, data augmentation
and image resizing, showing that better performance can be obtained through
somewhat simple procedures: for example, downscaling the images' resolution to
80% can lead to similar results with a 36% savings in extraction time and
dataset storage requirement. Code and trained models are available at
https://deep-vg-bench.herokuapp.com/.
Related papers
- A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling [54.05517338122698]
We propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives.
We also develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts.
Our proposed ReSFU framework consistently achieves satisfactory performance on different segmentation applications.
arXiv Detail & Related papers (2024-07-02T14:12:21Z) - Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM [6.242958695705305]
Implicit neural representation (INR) in combination with geometric rendering has been employed in real-time dense RGB-D SLAM.
We establish the first open-source benchmark framework to evaluate the performance of a wide spectrum of commonly used INRs and rendering functions.
We propose explicit hybrid encoding for high-fidelity dense grid mapping to comply with the RGB-D SLAM system.
arXiv Detail & Related papers (2024-03-28T14:59:56Z) - IOHexperimenter: Benchmarking Platform for Iterative Optimization
Heuristics [3.6980928405935813]
IOHexperimenter aims at providing an easy-to-use and highly customizable toolbox for benchmarking iterative optimizations.
IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer.
arXiv Detail & Related papers (2021-11-07T13:11:37Z) - Contextual Fine-to-Coarse Distillation for Coarse-grained Response
Selection in Open-Domain Conversations [48.046725390986595]
We propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
To evaluate the performance of our proposed model, we construct two new datasets based on the Reddit comments dump and Twitter corpus.
arXiv Detail & Related papers (2021-09-24T08:22:35Z) - Retrieval and Localization with Observation Constraints [12.010135672015704]
We propose an integrated visual re-localization method called RLOCS.
It combines image retrieval, semantic consistency and geometry verification to achieve accurate estimations.
Our method achieves many performance improvements on the challenging localization benchmarks.
arXiv Detail & Related papers (2021-08-19T06:14:33Z) - Elastic Architecture Search for Diverse Tasks with Different Resources [87.23061200971912]
We study a new challenging problem of efficient deployment for diverse tasks with different resources, where the resource constraint and task of interest corresponding to a group of classes are dynamically specified at testing time.
Previous NAS approaches seek to design architectures for all classes simultaneously, which may not be optimal for some individual tasks.
We present a novel and general framework, called Elastic Architecture Search (EAS), permitting instant specializations at runtime for diverse tasks with various resource constraints.
arXiv Detail & Related papers (2021-08-03T00:54:27Z) - Robust Image Retrieval-based Visual Localization using Kapture [10.249293519246478]
We present a versatile pipeline for visual localization that facilitates the use of different local and global features.
We evaluate our methods on eight public datasets where they rank top on all and first on many of them.
To foster future research, we release code, models, and all datasets used in this paper in the kapture format open source under a permissive BSD license.
arXiv Detail & Related papers (2020-07-27T21:10:35Z) - DC-NAS: Divide-and-Conquer Neural Architecture Search [108.57785531758076]
We present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures.
We achieve a $75.1%$ top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.
arXiv Detail & Related papers (2020-05-29T09:02:16Z) - Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
We introduce a comprehensive benchmark for local features and robust estimation algorithms.
Our pipeline's modular structure allows easy integration, configuration, and combination of different methods.
We show that with proper settings, classical solutions may still outperform the perceived state of the art.
arXiv Detail & Related papers (2020-03-03T15:20:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.