<i>AnyLoc</i>: Towards Universal Visual Place Recognition-Reference-Cited by-同舟云学术

AnyLoc: Towards Universal Visual Place Recognition

Published:2024-02 Issue:2 Volume:9 Page:1286-1293
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Keetha Nikhil¹^ORCID,Mishra Avneesh²^ORCID,Karhade Jay¹^ORCID,Jatavallabhula Krishna Murthy³^ORCID,Scherer Sebastian¹^ORCID,Krishna Madhava²^ORCID,Garg Sourav⁴^ORCID

Affiliation:

1. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

2. International Institute of Information Technology, Hyderabad, India

3. Massachusetts Institute of Technology, Cambridge, MA, USA

4. Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia

Funder

ARL

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/7083369/10360389/10361537-aam.pdf

Reference58 articles.

1. On the opportunities and risks of foundation models;Bommasani,2021

2. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

3. Deep Visual Geo-localization Benchmark

4. On the duality between contrastive and non-contrastive self-supervised learning;Garrido,2023

5. Objectives matter: Understanding the impact of self-supervised objectives on vision transformer representations;Shekhar;Proc. Int. Conf. Learn. Representations Workshop Math. Empirical Understanding Found. Models,2023

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