GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision
Recent multimodal large language models (MLLMs) have shown strong cross-modal understanding and coordinate generation abilities in visual grounding. However, transferring these abilities to remote sensing visual grounding (RSVG) remains challenging. High-resolution remote sensing images usually cover large-scale scenes, where targets are often extremely small and surrounded by numerous visually similar distractors. Meanwhile, queries often contain multiple clues, such as reference objects, spatial relations, and target attributes. Existing MLLM-based methods usually formulate RSVG as one-step
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- LinkedLinked via unknownEmbed the world: Multimodal AI for searchable aerial imagery at scale →
- LinkedLinked via arxiv author · 85%Dianyu Wang →
“GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision”
- LinkedLinked via arxiv author · 85%Yidan Zhang →
“GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision”
- LinkedLinked via arxiv author · 85%Peirong Zhang →
“GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision”
- LinkedLinked via arxiv author · 85%Xuyang Li →
“GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision”
- LinkedLinked via arxiv author · 85%Xiaoxuan Liu →
“GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision”
- LinkedLinked via arxiv author · 85%Lei Wang →
“GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision”
- FuzzySimilar title/name (fuzzy) · 84%roboflow/supervision →
“Fuzzy title match (0.92): “GeoSearcher: Anchor-Guided Progressive Reasoning for Remote ” ≈ “roboflow/supervision””