Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
Remote sensing (RS) cross-modal text-image retrieval has attracted extensive attention for its advantages of flexible input and efficient query. However, traditional methods ignore the characteristics of multi-scale and redundant targets in RS image, leading to the degradation of retrieval accuracy. To cope with the problem of multi-scale scarcity and target redundancy in RS multimodal retrieval task, we come up with a novel asymmetric multimodal feature matching network (AMFMN). Our model adapts to multi-scale feature inputs, favors multi-source retrieval methods, and can dynamically filter redundant features. AMFMN employs the multi-scale visual self-attention (MVSA) module to extract the salient features of RS image and utilizes visual features to guide the text representation. Furthermore, to alleviate the positive samples ambiguity caused by the strong intraclass similarity in RS image, we propose a triplet loss function with dynamic variable margin based on prior similarity of sample pairs. Finally, unlike the traditional RS image-text dataset with coarse text and higher intraclass similarity, we construct a fine-grained and more challenging Remote sensing Image-Text Match dataset (RSITMD), which supports RS image retrieval through keywords and sentence separately and jointly. Experiments on four RS text-image datasets demonstrate that the proposed model can achieve state-of-the-art performance in cross-modal RS text-image retrieval task.
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RSICD
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Cross-Modal Retrieval | RSICD | AMFMN | Mean Recall | 15.53% | # 8 | |
| Image-to-text R@1 | 5.21% | # 8 | ||||
| text-to-image R@1 | 4.08% | # 8 | ||||
| Cross-Modal Retrieval | RSITMD | AMFMN | Mean Recall | 29.72% | # 8 | |
| Image-to-text R@1 | 10.63% | # 8 | ||||
| text-to-imageR@1 | 11.51% | # 6 |
