  The development of accurate and scalable cross-modal image-text retrieval
methods, where queries from one modality (e.g., text) can be matched to archive
entries from another (e.g., remote sensing image) has attracted great attention
in remote sensing (RS). Most of the existing methods assume that a reliable
multi-modal training set with accurately matched text-image pairs is existing.
However, this assumption may not always hold since the multi-modal training
sets may include noisy pairs (i.e., textual descriptions/captions associated to
training images can be noisy), distorting the learning process of the retrieval
methods. To address this problem, we propose a novel unsupervised cross-modal
hashing method robust to the noisy image-text correspondences (CHNR). CHNR
consists of three modules: 1) feature extraction module, which extracts feature
representations of image-text pairs; 2) noise detection module, which detects
potential noisy correspondences; and 3) hashing module that generates
cross-modal binary hash codes. The proposed CHNR includes two training phases:
i) meta-learning phase that uses a small portion of clean (i.e., reliable) data
to train the noise detection module in an adversarial fashion; and ii) the main
training phase for which the trained noise detection module is used to identify
noisy correspondences while the hashing module is trained on the noisy
multi-modal training set. Experimental results show that the proposed CHNR
outperforms state-of-the-art methods. Our code is publicly available at
https://git.tu-berlin.de/rsim/chnr
