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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/LukasHaas/PIGEON

공식 코드

PIGEON: Predicting Image Geolocations

https://github.com/VedikaSrivastava/GeoMapper

공식 코드

Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping

https://github.com/VicenteVivan/geo-clip

공식 코드

GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

https://github.com/applied-machine-learning-lab/g3

공식 코드

G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models

https://github.com/cleopatra-itn/GeoWINE

공식 코드

GeoWINE: Geolocation based Wiki, Image,News and Event Retrieval

https://github.com/gastruc/osv5m

공식 코드

OpenStreetView-5M: The Many Roads to Global Visual Geolocation

https://github.com/jtheiner/semantic_geo_partitioning

공식 코드

Interpretable Semantic Photo Geolocation

https://github.com/nicolas-dufour/plonk

공식 코드

Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation

https://github.com/shramanpramanick/transformer_based_geo-localization

공식 코드

Where in the World is this Image? Transformer-based Geo-localization in the Wild

https://huggingface.co/geolocal/StreetCLIP

공식 코드

Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization

https://github.com/TIBHannover/GeoEstimation

https://github.com/gjacopo/poppysite

PlaNet - Photo Geolocation with Convolutional Neural Networks

https://github.com/kelsdoerksen/giga-connectivity

GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

https://github.com/ramanakshay/hierarchical-geoclip

GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.