计算机科学
语义鸿沟
判别式
情态动词
人工智能
一致性(知识库)
特征学习
利用
模式识别(心理学)
互补性(分子生物学)
模态(人机交互)
特征(语言学)
机器学习
数据挖掘
情报检索
图像检索
图像(数学)
哲学
生物
化学
高分子化学
遗传学
语言学
计算机安全
作者
Yafei Lv,Wei Xiong,Xiaohan Zhang,Yaqi Cui
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19: 1-5
被引量:8
标识
DOI:10.1109/lgrs.2021.3131592
摘要
With the increasing of cross-modal data, cross-modal retrieval has attracted more attention in remote sensing (RS), since it provides a more flexible and convenient way to obtain interesting information than traditional retrieval. However, existing methods cannot fully exploit the semantic information, which only focuses on the semantic consistency, and ignore the information complementarity between different modalities. In this letter, to bridge the modality gap, we propose a novel fusion-based correlation learning model (FCLM) for image-text retrieval in RS. Specifically, a cross-modal-fusion network is designed to capture the intermodality complementary information and fused feature. The fused knowledge is furtherly transferred to supervise the learning of modality-specific network by knowledge distillation, which is helpful in improving the discriminative ability of feature representation and enhancing the intermodality semantic consistency to solve the heterogeneity gap problem. Finally, extensive experiments have been conducted on a public dataset and experimental results have shown that the FCLM method is effective in performing cross-modal retrieval and outperforms several baseline methods.
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