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

arXiv: https://arxiv.org/abs/2302.00275 · HTML: https://mlsanity.com/paper/2302.00275/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [How Much Can CLIP Benefit Vision-and-Language Tasks?](https://mlsanity.com/paper/2107.06383/) (2021)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [Deep Visual Geo-localization Benchmark](https://mlsanity.com/paper/2204.03444/) (2022)
- [Revisiting IM2GPS in the Deep Learning Era](https://mlsanity.com/paper/1705.04838/) (2017)
- [Where in the World is this Image? Transformer-based Geo-localization in the Wild](https://mlsanity.com/paper/2204.13861/) (2022)
- [Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning](https://mlsanity.com/paper/2204.07596/) (2022)
- [Artificial Neural Networks Applied to Taxi Destination Prediction](https://mlsanity.com/paper/1508.00021/) (2015)
- [G^3: Geolocation via Guidebook Grounding](https://mlsanity.com/paper/2211.15521/) (2022)
- [DeepGeo: Photo Localization with Deep Neural Network](https://mlsanity.com/paper/1810.03077/) (2018)

## Built on by (most cited first, 30 of 41)

- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [PIGEON: Predicting Image Geolocations](https://mlsanity.com/paper/2307.05845/) (2023)
- [OpenStreetView-5M: The Many Roads to Global Visual Geolocation](https://mlsanity.com/paper/2404.18873/) (2024)
- [Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models](https://mlsanity.com/paper/2506.14674/) (2025)
- [GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model](https://mlsanity.com/paper/2406.18572/) (2024)
- [Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework](https://mlsanity.com/paper/2502.13759/) (2025)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation](https://mlsanity.com/paper/2412.06781/) (2024)
- [AddressCLIP: Empowering Vision-Language Models for City-wide Image Address Localization](https://mlsanity.com/paper/2407.08156/) (2024)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [Learning Cross-view Visual Geo-localization without Ground Truth](https://mlsanity.com/paper/2403.12702/) (2024)
- [Towards Interactive Global Geolocation Assistant](https://mlsanity.com/paper/2412.08907/) (2024)
- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [A citizen science toolkit to collect human perceptions of urban environments using open street view images](https://mlsanity.com/paper/2403.00174/) (2024)
- [Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering](https://mlsanity.com/paper/2411.14816/) (2024)
- [NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization](https://mlsanity.com/paper/2502.14638/) (2025)
- [SpotAgent: Grounding Visual Geo-localization in Large Vision-Language Models through Agentic Reasoning](https://mlsanity.com/paper/2602.09463/) (2026)
- [Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2601.00388/) (2026)
- [GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics](https://mlsanity.com/paper/2602.12617/) (2026)
- [GeoRC: A Benchmark for Geolocation Reasoning Chains](https://mlsanity.com/paper/2601.21278/) (2026)
- [GeoExplain: Multimodal Reasoning based on Hierarchy of Visual Information in Street View](https://mlsanity.com/paper/2506.16633/) (2025)
- [Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks](https://mlsanity.com/paper/2506.23481/) (2025)
- [AddressVLM: Cross-view Alignment Tuning for Image Address Localization using Large Vision-Language Models](https://mlsanity.com/paper/2508.10667/) (2025)
- [Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning](https://mlsanity.com/paper/2603.10463/) (2026)
- [Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models](https://mlsanity.com/paper/2404.06309/) (2024)
- [Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection](https://mlsanity.com/paper/2503.14754/) (2025)
- [VLM-Guided Visual Place Recognition for Planet-Scale Geo-Localization](https://mlsanity.com/paper/2507.17455/) (2025)
- [Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.01341/) (2025)
- [An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything](https://mlsanity.com/paper/2312.04063/) (2023)
- [Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks](https://mlsanity.com/paper/2412.20682/) (2024)

## Abstract (arXiv)

Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo. It is an unsolved problem relying on the ability to combine visual clues with general knowledge about the world to make accurate predictions across geographies. We present hrefhttps://huggingface.co/geolocal/StreetCLIPStreetCLIP, a robust, publicly available foundation model not only achieving state-of-the-art performance on multiple open-domain image geolocalization benchmarks but also doing so in a zero-shot setting, outperforming supervised models trained on more than 4 million images. Our method introduces a meta-learning approach for generalized zero-shot learning by pretraining CLIP from synthetic captions, grounding CLIP in a domain of choice. We show that our method effectively transfers CLIP's generalized zero-shot capabilities to the domain of image geolocalization, improving in-domain generalized zero-shot performance without finetuning StreetCLIP on a fixed set of classes.