HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning
Recent advances in Vision-Language Models (VLMs) have significantly improved image geo-localization, yet existing models remain susceptible to landmark bias, causing them to overlook geographical cues or form spurious correlations, ultimately resulting in inaccurate localization. To systematically investigate this issue, we first design two quantitative metrics, Bias Intensity (BI) and Bias Harmfulness (BH), to characterize the impact of landmarks exerted on model reasoning, and establish a comprehensive benchmark, LandmarkBias-3K. To mitigate landmark bias, we further propose an evidence-driv
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- PossiblePossibly related (embedding) · 47%Understanding large language models demands distinguishing human projection from machine cognition - Nature →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- FuzzyOverlapping authors or contributors · 62%rasbt/LLMs-from-scratch →
“Shared author/contributor keys: yin”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzySimilar title/name (fuzzy) · 59%rasbt/reasoning-from-scratch →
“Fuzzy title match (0.73): “HoloGeo: Mitigating Landmark Bias in Geo-localization via Ev” ≈ “rasbt/reasoning-from-scratch””
- LinkedLinked via arxiv author · 85%Pengcheng Zhou →
“HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning”
- LinkedLinked via arxiv author · 85%Xuanyu Liu →
“HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning”