EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
arXiv preprint arXiv:2505.10931 (2025)
4 Pith papers cite this work. Polarity classification is still indexing.
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GeoMamba with Geometric Feature Injection and Geometric Consistency Constraint modules achieves 63.3% mAP and 77.0% Rank-1 on the new FGOS-as dataset for unaligned optical-SAR fine-grained retrieval.
ASGNet combines a spectrum-guided non-local perception module, multi-source semantic extractor, and dense cross-layer decoder to outperform 21 prior methods on five polyp segmentation benchmarks.
A survey that organizes methods for cross-domain object detection into a taxonomy, analyzes domain shift across detection stages, and outlines persistent challenges.
citing papers explorer
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EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents
EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
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GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval
GeoMamba with Geometric Feature Injection and Geometric Consistency Constraint modules achieves 63.3% mAP and 77.0% Rank-1 on the new FGOS-as dataset for unaligned optical-SAR fine-grained retrieval.
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ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation
ASGNet combines a spectrum-guided non-local perception module, multi-source semantic extractor, and dense cross-layer decoder to outperform 21 prior methods on five polyp segmentation benchmarks.
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Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges
A survey that organizes methods for cross-domain object detection into a taxonomy, analyzes domain shift across detection stages, and outlines persistent challenges.