  Zero-shot classification of image scenes which can recognize the image scenes
that are not seen in the training stage holds great promise of lowering the
dependence on large numbers of labeled samples. To address the zero-shot image
scene classification, the cross-modal feature alignment methods have been
proposed in recent years. These methods mainly focus on matching the visual
features of each image scene with their corresponding semantic descriptors in
the latent space. Less attention has been paid to the contrastive relationships
between different image scenes and different semantic descriptors. In light of
the challenge of large intra-class difference and inter-class similarity among
image scenes and the potential noisy samples, these methods are susceptible to
the influence of the instances which are far from these of the same classes and
close to these of other classes. In this work, we propose a multi-level
cross-modal feature alignment method via contrastive learning for zero-shot
classification of remote sensing image scenes. While promoting the
single-instance level positive alignment between each image scene with their
corresponding semantic descriptors, the proposed method takes the
cross-instance contrastive relationships into consideration,and learns to keep
the visual and semantic features of different classes in the latent space apart
from each other. Extensive experiments have been done to evaluate the
performance of the proposed method. The results show that our proposed method
outperforms state of the art methods for zero-shot remote sensing image scene
classification. All the code and data are available at github
https://github.com/masuqiang/MCFA-Pytorch
