Enhancing Semantic Clarity: Discriminative and Fine-grained Information Mining for Remote Sensing Image-Text Retrieval

Liu, Yu; Chen, Haipeng; Liang, Yuheng; Yang, Yuheng; Yang, Xun; Lyu, Yingda

doi:10.24963/ijcai.2025/647

Enhancing Semantic Clarity: Discriminative and Fine-grained Information Mining for Remote Sensing Image-Text Retrieval

Yu Liu, Haipeng Chen, Yuheng Liang, Yuheng Yang, Xun Yang, Yingda Lyu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5815-5823.

https://doi.org/10.24963/ijcai.2025/647

Keywords

Machine Learning: ML: Representation learning
Machine Learning: ML: Multi-modal learning

Abstract

Remote sensing image-text retrieval is a fundamental task in remote sensing multimodal analysis, promoting the alignment of visual and language representations. The mainstream approaches commonly focus on capturing shared semantic representations between visual and textual modalities. However, the inherent characteristics of remote sensing image-text pairs lead to a semantic confusion problem, stemming from redundant visual representations and high inter-class similarity. To tackle this problem, we propose a novel Discriminative and Fine-grained Information Mining (DFIM) model, which aims to enhance semantic clarity by reducing visual redundancy and increasing the semantic gap between different classes. Specifically, the Dynamic Visual Enhancement (DVE) module adaptively enhances the visual discriminative features under the guidance of multimodal fusion information. Meanwhile, the Fine-grained Semantic Matching (FSM) module cleverly models the matching relationship between image regions and text words as an optimal transport problem, thereby refining intra-instance matching. Extensive experiments on two benchmark datasets justify the superiority of DFIM in terms of retrieval accuracy and visual interpretability over the leading methods.

Yu Liu, Haipeng Chen, Yuheng Liang, Yuheng Yang, Xun Yang, and Yingda Lyu. 2025. Enhancing Semantic Clarity: Discriminative and Fine-grained Information Mining for Remote Sensing Image-Text Retrieval. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5815–5823. https://doi.org/10.24963/ijcai.2025/647

@inproceedings{ijcai2025p647,
  title     = {Enhancing Semantic Clarity: Discriminative and Fine-grained Information Mining for Remote Sensing Image-Text Retrieval},
  author    = {Liu, Yu and Chen, Haipeng and Liang, Yuheng and Yang, Yuheng and Yang, Xun and Lyu, Yingda},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {5815--5823},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/647},
  url       = {https://doi.org/10.24963/ijcai.2025/647},
}

TY  - CPAPER
TI  - Enhancing Semantic Clarity: Discriminative and Fine-grained Information Mining for Remote Sensing Image-Text Retrieval
AU  - Liu, Yu
AU  - Chen, Haipeng
AU  - Liang, Yuheng
AU  - Yang, Yuheng
AU  - Yang, Xun
AU  - Lyu, Yingda
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 5815
EP  - 5823
DO  - 10.24963/ijcai.2025/647
UR  - https://doi.org/10.24963/ijcai.2025/647
ER  -