Learning Type-Aware Embeddings for Fashion Compatibility (1803.09196v2)

Abstract: Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3-5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries.

• The paper introduces a novel type-aware embedding technique that projects fashion items into type-specific spaces to capture both similarity and compatibility.
• It leverages a comprehensive dataset of 68,306 outfits with fine-grained item annotations, achieving a 3-5% improvement in compatibility prediction and fill-in-the-blank tasks.
• The approach offers practical benefits for e-commerce through enhanced outfit recommendations and signals potential for multi-type applications in other domains.

Learning Type-Aware Embeddings for Fashion Compatibility: An Overview

The paper "Learning Type-Aware Embeddings for Fashion Compatibility" by Vasileva et al. focuses on addressing the challenge of outfit composition in the domain of online fashion data. Specifically, it tackles the problem of learning embeddings that capture both item similarity and compatibility, with a particular emphasis on respecting item types.

Core Contributions and Methodology

The authors delineate a novel approach to learning image embeddings that respect item types and simultaneously encode the notions of similarity and compatibility. This is achieved via a type-aware embedding mechanism, which stands in contrast to conventional strategies that compress variability and enforce improper relational triangles by embedding all fashion items into a unified space.

1. Type-Aware Embeddings: The paper introduces a method to project items into type-specific embedding spaces derived from a shared general embedding. This approach allows diverse items, such as shoes and tops, to coexist in adaptable relational contexts — a necessary attribute for accurately capturing fashion compatibility.
2. Dataset: A new dataset comprising 68,306 outfits from the Polyvore platform has been curated. This dataset addresses deficiencies in existing fashion datasets by offering more comprehensive annotations, including fine-grained item types, which are critical for training and evaluating compatibility models.
3. Evaluation and Performance: The proposed type-aware embeddings achieve a 3-5% improvement over existing methods in two tasks: outfit compatibility prediction and fill-in-the-blank. These tasks are central to assessing a model's ability to effectively match and recommend outfits.

Implications and Future Directions

The paper's findings have several implications for both practical applications and future research:

• Practical Applications: The ability to recommend fashion items that are not only compatible but also respect type distinctions can significantly enhance user experience in e-commerce platforms. This model can drive personalized fashion recommendations by understanding nuanced fashion rules and user preferences.
• Theoretical Implications: The concept of type-aware embeddings could be extended beyond fashion to any domain where multitype compatibility is crucial, such as interior design and multimedia retrieval.
• Future Research Directions: Future work could explore further refinement of the embedding process by incorporating additional modalities, such as user feedback or contextual usage information. Additionally, scaling this approach to consider more complex scenarios, such as cultural or seasonal fashion influences, presents a compelling avenue for extending this research.

Overall, the paper sets a valuable precedent in the fashion compatibility landscape by innovating on established embedding techniques and proposing a scalable solution that rocks the fine line between similarity and compatibility with respect to item type. This is particularly useful in fashion, where these subtleties can make the difference between a mismatch and an on-trend ensemble.