Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences (1509.07473v1)

Abstract: 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.

• The paper introduces a Siamese CNN that learns a latent style space by leveraging heterogeneous dyadic co-occurrences from Amazon co-purchase data.
• Empirical evaluation shows significant performance improvements over baseline models, with superior AUC scores on large-scale datasets.
• The method demonstrates robust transferability to unseen clothing categories, offering practical insights for scalable fashion recommendation systems.

Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences

In the paper "Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences," the authors present a framework to address the problem of determining the compatibility of clothing items using computational methods. This approach leverages the massive amounts of user behavior data, particularly from a dataset compiled via Amazon.com co-purchase information, to learn and predict what clothing items can be deemed stylistically compatible.

Framework Overview

The proposed framework centers around the use of a Siamese Convolutional Neural Network (CNN) architecture to transform images of clothing items into a latent 'style space' where compatible items are positioned closer together. The network’s training data consists of 'heterogeneous dyadic co-occurrences,' implying that each training pair includes items from different but frequently co-purchased categories. This strategic sampling method is pivotal because it enables learning cross-category stylistic compatibility by not just grouping visually similar items but by interpreting nuanced notions of visual aesthetics that govern human clothing choices.

A key contribution of this research is the use of a Siamese CNN to directly learn a feature transformation, which contrasts traditional methods that rely on predefined attributes or solely emphasize visual similarity. This methodology is notably robust against label noise inherent in large datasets, such as misclassified categories, due to the robust nearest neighbor retrieval method involved.

Empirical Evaluation

The empirical evaluation using a large-scale dataset from Amazon.com relies on an extensive collection of clothing images, each linked with co-purchase information indicating compatibility. The results show that the models utilizing strategic sampling significantly outperform baseline models such as a vanilla ImageNet pre-trained CNN. The AUC scores indicate a considerable margin over na\"ive sampling techniques.

Moreover, the research thoroughly investigates the transferability of learned style features to unseen clothing categories. Three holdout experiments (shoes, shirts, and jeans) demonstrate that the framework retains a significant extent of transferability, highlighting its potential utility in dynamically evolving fashion domains or when new categories emerge.

Theoretical and Practical Implications

Theoretically, this research offers a meaningful extension to metric learning, traditionally constrained to same-category correspondences. It challenges models to interpret cross-category relationships, mirroring the complexity of human aesthetic preference systems. Practically, it sets the stage for applications in fashion recommendation systems, offering structural insights that could be incorporated into personalized shopping experiences.

Future Considerations

While the paper establishes a strong baseline in capturing cross-category clothing style compatibility, it opens several avenues for future exploration. Integrating user-specific data could refine personalized recommendations, while enhancing the model to adapt to rapidly changing fashion trends could align computational methods more closely with human stylistic evolution. Additionally, improving robustness to label noise and extending the framework beyond clothing to other domains like interior design offers exciting opportunities.

Overall, the paper illustrates a formidable step towards computational understanding and generation of style, underlined by empirical rigor and feasible applications in retail and fashion. This framework not only offers insight into current methodologies but also provides a bridge for future machine learning applications in the field of visual aesthetics.