  Deep neural networks have achieved promising progress in remote sensing (RS)
image classification, for which the training process requires abundant samples
for each class. However, it is time-consuming and unrealistic to annotate
labels for each RS category, given the fact that the RS target database is
increasing dynamically. Zero-shot learning (ZSL) allows for identifying novel
classes that are not seen during training, which provides a promising solution
for the aforementioned problem. However, previous ZSL models mainly depend on
manually-labeled attributes or word embeddings extracted from language models
to transfer knowledge from seen classes to novel classes. Besides, pioneer ZSL
models use convolutional neural networks pre-trained on ImageNet, which focus
on the main objects appearing in each image, neglecting the background context
that also matters in RS scene classification. To address the above problems, we
propose to collect visually detectable attributes automatically. We predict
attributes for each class by depicting the semantic-visual similarity between
attributes and images. In this way, the attribute annotation process is
accomplished by machine instead of human as in other methods. Moreover, we
propose a Deep Semantic-Visual Alignment (DSVA) that take advantage of the
self-attention mechanism in the transformer to associate local image regions
together, integrating the background context information for prediction. The
DSVA model further utilizes the attribute attention maps to focus on the
informative image regions that are essential for knowledge transfer in ZSL, and
maps the visual images into attribute space to perform ZSL classification. With
extensive experiments, we show that our model outperforms other
state-of-the-art models by a large margin on a challenging large-scale RS scene
classification benchmark.
