Lost Your Style? Navigating with Semantic-Level Approach for
  Text-to-Outfit Retrieval
Junkyu Jang Eugene Hwang Sung-Hyuk Park
Fashion stylists have historically bridged the gap between consumers' desires
and perfect outfits, which involve intricate combinations of colors, patterns,
and materials. Although recent advancements in fashion recommendation systems
have made strides in outfit compatibility prediction and complementary item
retrieval, these systems rely heavily on pre-selected customer choices.
Therefore, we introduce a groundbreaking approach to fashion recommendations:
text-to-outfit retrieval task that generates a complete outfit set based solely
on textual descriptions given by users. Our model is devised at three semantic
levels-item, style, and outfit-where each level progressively aggregates data
to form a coherent outfit recommendation based on textual input. Here, we
leverage strategies similar to those in the contrastive language-image
pretraining model to address the intricate-style matrix within the outfit sets.
Using the Maryland Polyvore and Polyvore Outfit datasets, our approach
significantly outperformed state-of-the-art models in text-video retrieval
tasks, solidifying its effectiveness in the fashion recommendation domain. This
research not only pioneers a new facet of fashion recommendation systems, but
also introduces a method that captures the essence of individual style
preferences through textual descriptions.