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The emergence of online influencers, the explosion of
video content, and the massive amount of movie collections
have served as an advertising vehicle for the fashion industry.
This trend has created the need for automated methods
that recognize people’s outfit in such image and video collections.
However, existing computer vision solutions for
fashion recognition require an enormous amount of labeled
data for training, which is prohibitively expensive. In this
work, we propose an approach to build clothing recognition
models for real-world scenarios. Our approach exploits
images from the Amazon Catalog as training data.
By using the catalog data as an additional training source,
we boost the recognition accuracy on the challenging real
world images of the DeepFashion dataset achieving stateof-
the-art performance. We introduce the first dataset for
clothing recognition in movies. In this scenario, we find
that the use of catalog data for training becomes even more
crucial, as it provides an accuracy boost of 10%.
The emergence of online influencers, the explosion of
video content, and the massive amount of movie collections
have served as an advertising vehicle for the fashion industry.
This trend has created the need for automated methods
that recognize people’s outfit in such image and video collections.
However, existing computer vision solutions for
fashion recognition require an enormous amount of labeled
data for training, which is prohibitively expensive. In this
work, we propose an approach to build clothing recognition
models for real-world scenarios. Our approach exploits
images from the Amazon Catalog as training data.
By using the catalog data as an additional training source,
we boost the recognition accuracy on the challenging real
world images of the DeepFashion dataset achieving stateof-
the-art performance. We introduce the first dataset for
clothing recognition in movies. In this scenario, we find
that the use of catalog data for training becomes even more
crucial, as it provides an accuracy boost of 10%.
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