  Extracting and fusing part features have become the key of fined-grained
image recognition. Recently, Non-local (NL) module has shown excellent
improvement in image recognition. However, it lacks the mechanism to model the
interactions between multi-scale part features, which is vital for fine-grained
recognition. In this paper, we propose a novel cross-layer non-local (CNL)
module to associate multi-scale receptive fields by two operations. First, CNL
computes correlations between features of a query layer and all response
layers. Second, all response features are weighted according to the
correlations and are added to the query features. Due to the interactions of
cross-layer features, our model builds spatial dependencies among multi-level
layers and learns more discriminative features. In addition, we can reduce the
aggregation cost if we set low-dimensional deep layer as query layer.
Experiments are conducted to show our model achieves or surpasses
state-of-the-art results on three benchmark datasets of fine-grained
classification. Our codes can be found at github.com/FouriYe/CNL-ICIP2020.
