  Fashion landmarks are functional key points defined on clothes, such as
corners of neckline, hemline, and cuff. They have been recently introduced as
an effective visual representation for fashion image understanding. However,
detecting fashion landmarks are challenging due to background clutters, human
poses, and scales. To remove the above variations, previous works usually
assumed bounding boxes of clothes are provided in training and test as
additional annotations, which are expensive to obtain and inapplicable in
practice. This work addresses unconstrained fashion landmark detection, where
clothing bounding boxes are not provided in both training and test. To this
end, we present a novel Deep LAndmark Network (DLAN), where bounding boxes and
landmarks are jointly estimated and trained iteratively in an end-to-end
manner. DLAN contains two dedicated modules, including a Selective Dilated
Convolution for handling scale discrepancies, and a Hierarchical Recurrent
Spatial Transformer for handling background clutters. To evaluate DLAN, we
present a large-scale fashion landmark dataset, namely Unconstrained Landmark
Database (ULD), consisting of 30K images. Statistics show that ULD is more
challenging than existing datasets in terms of image scales, background
clutters, and human poses. Extensive experiments demonstrate the effectiveness
of DLAN over the state-of-the-art methods. DLAN also exhibits excellent
generalization across different clothing categories and modalities, making it
extremely suitable for real-world fashion analysis.
