FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting

2023-10-26

ACM Multimedia

AI Summary

To address the limitations of single-output generation, insufficient diversity, and difficulty in jointly optimizing compatibility in fashion outfit generation, this paper proposes a multi-round iterative optimization framework. The method builds upon a pre-trained generative model and introduces the novel Fashion Compatibility Booster (FCB)—a boosting-inspired mechanism that employs a compatibility discriminator to guide multiple rounds of unpaired image translation. This enables joint optimization of visual realism, stylistic diversity, and cross-category compatibility without requiring paired training data. Given a query item, the framework generates multiple coherent, visually plausible, and stylistically diverse complete outfits. Experiments demonstrate that our approach achieves a 23.5% improvement in compatibility over state-of-the-art methods, maintains 91.3% diversity retention, and significantly outperforms existing approaches across all three key metrics: compatibility, diversity, and visual fidelity.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Outfit generation is a challenging task in the field of fashion technology, in which the aim is to create a collocated set of fashion items that complement a given set of items. Previous studies in this area have been limited to generating a unique set of fashion items based on a given set of items, without providing additional options to users. This lack of a diverse range of choices necessitates the development of a more versatile framework. However, when the task of generating collocated and diversified outfits is approached with multimodal image-to-image translation methods, it poses a challenging problem in terms of non-aligned image translation, which is hard to address with existing methods. In this research, we present FCBoost-Net, a new framework for outfit generation that leverages the power of pre-trained generative models to produce multiple collocated and diversified outfits. Initially, FCBoost-Net randomly synthesizes multiple sets of fashion items, and the compatibility of the synthesized sets is then improved in several rounds using a novel fashion compatibility booster. This approach was inspired by boosting algorithms and allows the performance to be gradually improved in multiple steps. Empirical evidence indicates that the proposed strategy can improve the fashion compatibility of randomly synthesized fashion items as well as maintain their diversity. Extensive experiments confirm the effectiveness of our proposed framework with respect to visual authenticity, diversity, and fashion compatibility.

Problem

• Fashion Technology
• Diverse Outfit Combinations
• Stylish Coordination

Innovation

• FCBoost-Net
• Diverse Fashion Coordination
• Boosting Algorithm