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Synapse
September 16, 2025

Zero-shot adapter framework for cross-modal classification of remote sensing imagery

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Authors

YSYong SunQCQijin ChengWXWeijian Xie

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Overview

Novel framework enhances classification performance in remote sensing using zero-shot learning and LLMs.

Key Points

  • The framework achieves improved zero-shot and few-shot classification performance in resource-constrained settings.
  • It utilizes an LLM-Augmented Prompt Generalization to enhance semantic depth in remote sensing tasks.
  • A Proxy-Enhanced Support Set Construction mechanism addresses the scarcity of annotated datasets.
  • The integration of a Multi-Granularity Feature Cache allows effective feature storage for better image and text alignment.

Cite This Study

Sun et al. (2025) studied this question.

synapsesocial.com/papers/68d4565431b076d99fa5b091https://doi.org/10.21203/rs.3.rs-7388684/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Low-data cross-modal adaptation for remote sensing with proxy-enhanced multi-granularity feature caching2026 · 1 citations
  2. 2Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification2024
  3. 3Meta-Prompting with Open-Source Language Models for Zero-Shot Scene Classification in Remote Sensing2026
  4. 4Exploring the Potential of VLMs in Remote Sensing through Prompt Optimization2025
  5. 5Exploring the Potential of VLMs in Remote Sensing through Prompt Optimization2025