Papers › Reducing Semantic Confusion: Scene-aware Aggregation Network for Remote Sensing...
Reducing Semantic Confusion: Scene-aware Aggregation Network for Remote Sensing Cross-modal Retrieval
Jiancheng Pan, Qing Ma, Cong Bai
Recently, remote sensing cross-modal retrieval has received incredible attention from researchers. However, the unique nature of remote-sensing images leads to many semantic confusion zones in the semantic space, which greatly affects retrieval performance. We propose a novel scene-aware aggregation network (SWAN) to reduce semantic confusion by improving scene perception capability. In visual representation, a visual multiscale fusion module (VMSF) is presented to fuse visual features with different scales as a visual representation backbone. Meanwhile, a scene fine-grained sensing module (SFGS) is proposed to establish the associations of salient features at different granularity. A scene-aware visual aggregation representation is formed by the visual information generated by these two modules. In textual representation, a textual coarse-grained enhancement module (TCGE) is designed to enhance the semantics of text and to align visual information. Furthermore, as the diversity and differentiation of remote sensing scenes weaken the understanding of scenes, a new metric, namely, scene recall is proposed to measure the perception of scenes by evaluating scene-level retrieval performance, which can also verify the effectiveness of our approach in reducing semantic confusion. By performance comparisons, ablation studies and visualization analysis, we validated the effectiveness and superiority of our approach on two datasets, RSICD and RSITMD. The source code is available at https://github.com/kinshingpoon/SWAN-pytorch.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Modal Retrieval | RSICD | SWAN | Image-to-text R@1 | 7.41% | #8 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSICD | SWAN | Mean Recall | 20.61% | #8 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSICD | SWAN | text-to-image R@1 | 5.56% | #8 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSITMD | SWAN | Image-to-text R@1 | 13.35% | #9 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSITMD | SWAN | Mean Recall | 34.11% | #9 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSITMD | SWAN | text-to-imageR@1 | 11.24% | #9 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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