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ReCoTR: Reducing Semantic Cognitive Shift via Dual-Consensus Token Compression for Remote Sensing Image-Text

Jirui Huang, Yaxiong Chen, Chuang Du

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 11, 2026
    PubMed
    Summary
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    This study introduces ReCoTR, a novel framework for remote sensing (RS) data retrieval. ReCoTR enhances vision-language model (VLM) performance by addressing semantic shifts in RS images, improving urban governance and environmental monitoring.

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Vision-language models (VLMs) show promise for remote sensing (RS) data analysis.
    • Semantic shift in RS images challenges VLM transferability and performance.
    • Existing methods struggle with region-level bias and background noise in RS data.

    Purpose of the Study:

    • To develop an enhanced CLIP-based framework, ReCoTR, for cross-modal retrieval in remote sensing.
    • To address semantic shift, region-level granularity bias, and contextual semantic drift in RS imagery.
    • To improve the accuracy and robustness of semantic understanding for large-scale RS datasets.

    Main Methods:

    • Proposed ReCoTR framework utilizing a Dual Consensus Token Evaluation (DCTE) module.

    Related Experiment Videos

  • DCTE employs a mixture-of-experts strategy to fuse inter-modal semantic consensus and intra-modal structural consistency.
  • Introduced Semantic Confidence Token Compression (SCTC) module to filter and aggregate semantically relevant tokens, reducing noise.

    • Main Results:

    • ReCoTR demonstrated superior performance on bidirectional image-text retrieval tasks across three benchmark RS datasets.
    • The framework effectively mitigates region-level granularity bias and contextual semantic drift.
    • ReCoTR shows robustness in handling background noise and improving semantic alignment in RS data.

    Conclusions:

    • ReCoTR significantly enhances cross-modal retrieval for remote sensing data.
    • The proposed DCTE and SCTC modules effectively address key challenges in RS image-text understanding.
    • ReCoTR offers a robust solution for applications in urban governance, environmental monitoring, and disaster response.