Name
Abstract | ||
|---|---|---|
Remote sensing (RS) crossmodal text-image retrieval has become a research hotspot in recent years for its application in semantic localization. However, since multiple inferences on slices are demanded in semantic localization, designing a crossmodal retrieval model with less computation but well performance becomes an emergent and challenging task. In this article, considering the characteristics of multi-scale and target redundancy in RS, a concise but effective crossmodal retrieval model (LW-MCR) is designed. The proposed model incorporates multi-scale information and dynamically filters out redundant features when encoding RS image, while text features are obtained via lightweight group convolution. To improve the retrieval performance of LW-MCR, we come up with a novel hidden supervised optimization method based on knowledge distillation. This method enables the proposed model to acquire dark knowledge of the multi-level layers and representation layers in the teacher network, which significantly improves the accuracy of our lightweight model. Finally, on the basis of contrast learning, we present a method employing unlabeled data to boost the performance of RS retrieval model further. The experiment results on four RS image-text datasets demonstrate the efficiency of LW-MCR in RS crossmodal retrieval (RSCR) tasks. We have released some codes of the semantic localization and made it open to access at https://github.com/xiaoyuan1996/retrievalSystem. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3124252 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Task analysis, Computational modeling, Data models, Semantics, Remote sensing, Feature extraction, Optimization methods, Contrast learning, cross-modal remote sensing (RS) text-image retrieval, knowledge distillation, lightweight retrieval, semantic localization | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Yuan | 1 | 0 | 1.69 |
| Wenkai Zhang | 2 | 0 | 4.73 |
| Xuee Rong | 3 | 0 | 1.01 |
| Xuan Li | 4 | 9 | 1.93 |
| Jialiang Chen | 5 | 0 | 0.34 |
| Hongqi Wang | 6 | 0 | 1.69 |
| Kun Fu | 7 | 414 | 57.81 |
| Xian Sun | 8 | 0 | 8.45 |