Detecting dense landmarks for diverse clothes, as a fundamental technique for
clothes analysis, has attracted increasing research attention due to its huge
application potential. However, due to the lack of modeling underlying semantic
layout constraints among landmarks, prior works often detect ambiguous and
structure-inconsistent landmarks of multiple overlapped clothes in one person.
In this paper, we propose to seamlessly enforce structural layout relationships
among landmarks on the intermediate representations via multiple stacked
layout-graph reasoning layers. We define the layout-graph as a hierarchical
structure including a root node, body-part nodes (e.g. upper body, lower body),
coarse clothes-part nodes (e.g. collar, sleeve) and leaf landmark nodes (e.g.
left-collar, right-collar). Each Layout-Graph Reasoning(LGR) layer aims to map
feature representations into structural graph nodes via a Map-to-Node module,
performs reasoning over structural graph nodes to achieve global layout
coherency via a layout-graph reasoning module, and then maps graph nodes back
to enhance feature representations via a Node-to-Map module. The layout-graph
reasoning module integrates a graph clustering operation to generate
representations of intermediate nodes (bottom-up inference) and then a graph
deconvolution operation (top-down inference) over the whole graph. Extensive
experiments on two public fashion landmark datasets demonstrate the superiority
of our model. Furthermore, to advance the fine-grained fashion landmark
research for supporting more comprehensive clothes generation and attribute
recognition, we contribute the first Fine-grained Fashion Landmark Dataset
(FFLD) containing 200k images annotated with at most 32 key-points for 13
clothes types.

本文提出了一种基于多层次的布局图推理层的方法，通过在中间表示中 na 无缝执行结构布局关系来检测多样化服装的密集关键点。实验结果表明，该模型具有优越性能，并贡献出了第一个细粒度的时尚标志物数据集以支持更全面的衣服生成和属性识别。