Abstract
In this paper, we investigate ways of conducting a detailed fashion search using query images and attributes. A
credible fashion search platform should be able to (1) find
images that share the same attributes as the query image,
(2) allow users to manipulate certain attributes, e.g. replace collar attribute from round to v-neck, and (3) handle region-specific attribute manipulations, e.g. replacing
the color attribute of the sleeve region without changing the
color attribute of other regions. A key challenge to be addressed is that fashion products have multiple attributes and
it is important for each of these attributes to have representative features. To address these challenges, we propose the
FashionSearchNet which uses a weakly supervised localization method to extract regions of attributes. By doing so,
unrelated features can be ignored thus improving the similarity learning. Also, FashionSearchNet incorporates a new
procedure that enables region awareness to be able to handle region-specific requests. FashionSearchNet outperforms
the most recent fashion search techniques and is shown to
be able to carry out different search scenarios using the dynamic queries.