1,048 research outputs found
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
With the rapid proliferation of smart mobile devices, users now take millions
of photos every day. These include large numbers of clothing and accessory
images. We would like to answer questions like `What outfit goes well with this
pair of shoes?' To answer these types of questions, one has to go beyond
learning visual similarity and learn a visual notion of compatibility across
categories. In this paper, we propose a novel learning framework to help answer
these types of questions. The main idea of this framework is to learn a feature
transformation from images of items into a latent space that expresses
compatibility. For the feature transformation, we use a Siamese Convolutional
Neural Network (CNN) architecture, where training examples are pairs of items
that are either compatible or incompatible. We model compatibility based on
co-occurrence in large-scale user behavior data; in particular co-purchase data
from Amazon.com. To learn cross-category fit, we introduce a strategic method
to sample training data, where pairs of items are heterogeneous dyads, i.e.,
the two elements of a pair belong to different high-level categories. While
this approach is applicable to a wide variety of settings, we focus on the
representative problem of learning compatible clothing style. Our results
indicate that the proposed framework is capable of learning semantic
information about visual style and is able to generate outfits of clothes, with
items from different categories, that go well together.Comment: ICCV 201
Creating Capsule Wardrobes from Fashion Images
We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.Comment: Accepted to CVPR 201
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