  Fashion landmark detection is a challenging task even using the current deep
learning techniques, due to the large variation and non-rigid deformation of
clothes. In order to tackle these problems, we propose Spatial-Aware Non-Local
(SANL) block, an attentive module in deep neural network which can utilize
spatial information while capturing global dependency. Actually, the SANL block
is constructed from the non-local block in the residual manner which can learn
the spatial related representation by taking a spatial attention map from
Grad-CAM. We then establish our fashion landmark detection framework on feature
pyramid network, equipped with four SANL blocks in the backbone. It is
demonstrated by the experimental results on two large-scale fashion datasets
that our proposed fashion landmark detection approach with the SANL blocks
outperforms the current state-of-the-art methods considerably. Some
supplementary experiments on fine-grained image classification also show the
effectiveness of the proposed SANL block.
