  With a growing demand for the search by image, many works have studied the
task of fashion instance-level image retrieval (FIR). Furthermore, the recent
works introduce a concept of fashion attribute manipulation (FAM) which
manipulates a specific attribute (e.g color) of a fashion item while
maintaining the rest of the attributes (e.g shape, and pattern). In this way,
users can search not only "the same" items but also "similar" items with the
desired attributes. FAM is a challenging task in that the attributes are hard
to define, and the unique characteristics of a query are hard to be preserved.
Although both FIR and FAM are important in real-life applications, most of the
previous studies have focused on only one of these problem. In this study, we
aim to achieve competitive performance on both FIR and FAM. To do so, we
propose a novel method that converts a query into a representation with the
desired attributes. We introduce a new idea of attribute manipulation at the
feature level, by matching the distribution of manipulated features with real
features. In this fashion, the attribute manipulation can be done independently
from learning a representation from the image. By introducing the feature-level
attribute manipulation, the previous methods for FIR can perform attribute
manipulation without sacrificing their retrieval performance.
