Scalable and Explainable Outfit Generation

Lorbert, Alexander; Neiman, David; Poznanski, Arik; Oks, Eduard; Davis, Larry S.

doi:10.1109/cvprw53098.2021.00439

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Previous works have relied on visual, textual information and fashion categories for creating representations of garments in outfits. Transfer learning is generally being used for extracting visual information from the garment's images, either with feature extraction (FX) from ImageNet pretrained models [7,8] or by end-to-end fine-tuning (E2E) for OC b [9,2,10,1,11]. E2E tends to outperform FX-ImageNet since the visual features are trained to specialize on the target domain and task.…”

Section: Related Workmentioning

confidence: 99%

“…We propose the Visual InCompatibility TransfORmer, or VICTOR, a multi-tasking, Transformer-based architecture that is trained to predict the overall OC r score and detect mismatching garments in an outfit. Previous works on OC b either rely on feature extraction from computer vision models pre-trained on ImageNet [7,8] or end-to-end (E2E) fine-tuning [9,2,10,1,11].…”

Section: Introductionmentioning

confidence: 99%

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VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

Papadopoulos

Koutlis

Papadopoulos

et al. 2023

Journal of Visual Communication and Image Representation

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

Papadopoulos

Koutlis

Papadopoulos

et al. 2023

Journal of Visual Communication and Image Representation

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“…For our recommendation use-cases, we use a standard Transformer encoder [3,5,11] trained with casual language model (CLM) approach, although the approach is oblivious of the training logic and it can be trained with the masked language modeling (MLM) as well [3,10]. For the personalized outfit generation use-cases, we use variations of the standard encoder-decoder (sequence-to-sequence) Transformer architecture [11] as in [2,6]. The model is trained on the same source of sequence interactions.…”

Section: Model Architecturementioning

confidence: 99%

Reusable Self-Attention Recommender Systems in Fashion Industry Applications

Celikik¹,

Ramallo²,

Wasilewski³

2022

Proceedings of the 16th ACM Conference on Recommender Systems

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A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, we leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions. We present both offline and online experimental results demonstrating substantial improvements in customer retention and engagement.

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Study of AI-Driven Fashion Recommender Systems

Shirkhani,

Mokayed,

Saini

et al. 2023

SN COMPUT. SCI.

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The rising diversity, volume, and pace of fashion manufacturing pose a considerable challenge in the fashion industry, making it difficult for customers to pick which product to purchase. In addition, fashion is an inherently subjective, cultural notion and an ensemble of clothing items that maintains a coherent style. In most of the domains in which Recommender Systems are developed (e.g., movies, e-commerce, etc.), the similarity evaluation is considered for recommendation. Instead, in the Fashion domain, compatibility is a critical factor. In addition, raw visual features belonging to product representations that contribute to most of the algorithm’s performances in the Fashion domain are distinguishable from the metadata of the products in other domains. This literature review summarizes various Artificial Intelligence (AI) techniques that have lately been used in recommender systems for the fashion industry. AI enables higher-quality recommendations than earlier approaches. This has ushered in a new age for recommender systems, allowing for deeper insights into user-item relationships and representations and the discovery patterns in demographical, textual, virtual, and contextual data. This work seeks to give a deeper understanding of the fashion recommender system domain by performing a comprehensive literature study of research on this topic in the past 10 years, focusing on image-based fashion recommender systems taking AI improvements into account. The nuanced conceptions of this domain and their relevance have been developed to justify fashion domain-specific characteristics.

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Scalable and Explainable Outfit Generation

Cited by 6 publications

References 4 publications

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

VICTOR: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training

Reusable Self-Attention Recommender Systems in Fashion Industry Applications

Study of AI-Driven Fashion Recommender Systems

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