  Clothing segmentation and fine-grained attribute recognition are challenging
tasks at the crossing of computer vision and fashion, which segment the entire
ensemble clothing instances as well as recognize detailed attributes of the
clothing products from any input human images. Many new models have been
developed for the tasks in recent years, nevertheless the segmentation accuracy
is less than satisfactory in case of layered clothing or fashion products in
different scales. In this paper, a new DEtection TRansformer (DETR) based
method is proposed to segment and recognize fine-grained attributes of ensemble
clothing instances with high accuracy. In this model, we propose a
\textbf{multi-layered attention module} by aggregating features of different
scales, determining the various scale components of a single instance, and
merging them together. We train our model on the Fashionpedia dataset and
demonstrate our method surpasses SOTA models in tasks of layered clothing
segmentation and fine-grained attribute recognition.
