Fashion Compatibility Research Articles

Multi-order attributes information fusion via hypergraph matching for popular fashion compatibility analysis

Popular fashion compatibility modeling aims to quantitatively assess the compatibility of a set of wearable items for everyday pairings and clothing purchases to assist human decision-making, which has garnered extensive academic attention. Earlier approaches that studied garments as a whole could only discern loose relationships between items, resulting in low accuracy and poor interpretability. Although existing state-of-the-art methods attempt to reveal the mechanism of garment compatibility at a fine-grained level by quantifying pairwise attribute compatibility, they overlook the fact that multi-attribute combinations between apparel items tend to play a more salient role in compatibility. Considering the complex and high-order characteristics of compatibility data, we propose a network named MAIF to deeply mine and reveal the intricate compatibility mechanisms of clothing by fusing multi-order attributes compatibility information through hypergraph matching. Specifically, we use the compatibility modeling of top-item and bottom-item as an example. First, we construct an adaptive hypergraph representation module to model the multi-attribute association combinations of individual clothing items and fuse single-attribute variable information to form multi-order attribute association information. Second, we learn the multi-order compatibility information of attributes between clothing items through spatial similarity matching. Considering the varying compatibility impacts caused by different attribute combinations, we construct a dynamic cross-plot matching mechanism to model the impact weights of multi-order attribute compatibility information. Finally, personalized ranking loss is designed to optimize the model parameters using fashion context information. Experimental and user survey studies conducted on the FashionVC and Polyvore-Maryland datasets verified the validity and superiority of MAIF in accurately assessing apparel compatibility, demonstrating its ability to interpret multi-order attribute compatibility information.

Correlation-aware Cross-modal Attention Network for Fashion Compatibility Modeling in UGC Systems

Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multimodal perspective with promising progress, they still fail to fully leverage the interactions between multimodal information or ignore the item-item contextual connectivities of intra-outfit. In this paper, a novel fashion compatibility modeling scheme is proposed based on Correlation-aware Cross-modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multimodal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multimodal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item-item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.

Explainable fashion compatibility Prediction: An Attribute-Augmented neural framework

Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.

A review of explainable fashion compatibility modeling methods

The paper reviews methods used in the fashion compatibility recommendation domain. We select methods based on reproducibility, explainability, and novelty aspects and then organize them chronologically and thematically. We presented general characteristics of publicly available datasets that are related to the fashion compatibility recommendation task. Finally, we analyzed the representation bias of datasets, fashion-based algorithms’ sustainability, and explainable model assessment. The paper describes practical problem explanations, methodologies, and published datasets that may serve as an inspiration for further research. The proposed structure of the survey organizes knowledge in the fashion recommendation domain and will be beneficial for those who want to learn the topic from scratch, expand their knowledge, or find a new field for research. Furthermore, the information included in this paper could contribute to developing an effective and ethical fashion-based recommendation system.

Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework.

The field of fashion compatibility learning has attracted great attention from both the academic and industrial communities in recent years. Many studies have been carried out for fashion compatibility prediction, collocated outfit recommendation, artificial intelligence (AI)-enabled compatible fashion design, and related topics. In particular, AI-enabled compatible fashion design can be used to synthesize compatible fashion items or outfits to improve the design experience for designers or the efficacy of recommendations for customers. However, previous generative models for collocated fashion synthesis have generally focused on the image-to-image translation between fashion items of upper and lower clothing. In this article, we propose a novel outfit generation framework, i.e., OutfitGAN, with the aim of synthesizing a set of complementary items to compose an entire outfit, given one extant fashion item and reference masks of target synthesized items. OutfitGAN includes a semantic alignment module (SAM), which is responsible for characterizing the mapping correspondence between the existing fashion items and the synthesized ones, to improve the quality of the synthesized images, and a collocation classification module (CCM), which is used to improve the compatibility of a synthesized outfit. To evaluate the performance of our proposed models, we built a large-scale dataset consisting of 20 000 fashion outfits. Extensive experimental results on this dataset show that our OutfitGAN can synthesize photo-realistic outfits and outperform the state-of-the-art methods in terms of similarity, authenticity, and compatibility measurements.

Research on type-aware fashion compatibility prediction based on a hybrid attention mechanism

Research on type-aware fashion compatibility prediction based on a hybrid attention mechanism

Toward Fashion Intelligence in the Big Data Era: State-of-the-Art and Future Prospects

Fashion, as a popular aesthetic expression, is often conveyed in a specific context, in particular in clothing, footwear, and accessories. Attributed to its enormous economic potential, intelligent fashion analysis has attracted focused attention from both academia and industrial practitioners in recent years. In this research, we provide a comprehensive review of fashion analysis-related tasks, which include fashion detection, fashion parsing, fashion retrieval, fashion style learning, fashion compatibility learning, fashion attribute prediction, and fashion generation. We investigated state-of-the-art articles from 1990 to the present, and provided a new taxonomy of extant research topics over these articles. We then summarized most representative fashion datasets with detailed statistical information. The coupled topics, methods, and features with respect to different levels of fashion analysis are also highlighted. Finally, remaining challenges and open issues are discussed in order to provide guidance for future studies.

TryonCM2: Try-on-Enhanced Fashion Compatibility Modeling Framework.

Recently, fashion compatibility modeling, which can score the matching degree of several complementary fashion items, has gained increasing research attention. Previous studies have primarily learned the features of fashion items and utilize their interaction as the fashion compatibility. However, the try-on looking of an outfit help us to learn the fashion compatibility in a combined manner, where items are spatially distributed and partially covered by other items. Inspired by this, we design a try-on-enhanced fashion compatibility modeling framework, named TryonCM2, which incorporates the try-on appearance with the item interaction to enhance the fashion compatibility modeling. Specifically, we treat each outfit as a sequence of items and adopt the bidirectional long short-term memory (LSTM) network to capture the latent interaction of fashion items. Meanwhile, we synthesize a try-on template image to depict the try-on appearance of an outfit. And then, we regard the outfit as a sequence of multiple image stripes, i.e., local content, of the try-on template, and adopt the bidirectional LSTM network to capture the contextual structure in the try-on appearance. Ultimately, we combine the fashion compatibility lying in the item interaction and try-on appearance as the final compatibility of the outfit. Both the objective and subjective experiments on the existing FOTOS dataset demonstrate the superiority of our framework over the state-of-the-art methods.

Multimodal High-Order Relationship Inference Network for Fashion Compatibility Modeling in Internet of Multimedia Things

Recent progress in artificial intelligence (AI) have broadened various intelligent application scenarios on the Internet of Multimedia Things (IoMT). Due to the urgent demands for intelligence in the online fashion industry, using AI techniques to explore user’s clothing collocations from fashion data generated by the IoMT system is a challenging task. Under the background, fashion compatibility modeling (FCM), which aims to estimate the matching degree of a given outfit, has attracted great attention in the multimedia analysis field. However, most of the studies often fail to fully leverage multimodal content or ignore the sparse associations between fashion items. In this paper, we propose a novel multimodal high-order relationship inference network (MHRIN) for fashion compatibility modeling task. In MHRIN, we focus on enriching multimodal representations of fashion items by means of incorporating the category correlations and injecting high-order item-item connectivity. Concretely, considering that fashion collocations depend on the semantic relevance patterns between categories, we design a category correlations learning module to adaptively learn category representations. On this basis, multiple modality representations are aggregated by a hierarchical multimodal fusion module to generate visual-semantic embeddings. To address the item-item matching interactions issue, we further refine the final representations by a high-order message propagation module to absorb rich connection information. Experiments on the publicly available dataset demonstrate the superiority of our MHRIN over state-of-the-art methods.

PFNet: Attribute-aware personalized fashion editing with explainable fashion compatibility analysis

Attribute-aware editing provides a feasible way for users to participate in fashion design. In a sense, an explainable model of fashion compatibility can assist users to perceive the fashionability of their design. However, previous fashion editing researches are opaque in compatibility and rely on labels to manipulate the editing of specific coarse-grained attributes. Consequently, we propose a novel attribute-aware personalized fashion editing network with explainable fashion compatibility modeling, named PFNet, which can simultaneously decouple entangled attributes to make them editable and generate an attribute-wise compatibility explanation for fashion design. In PFNet, we propose an unsupervised garment attribute decoupling network, which independently encodes attributes by hierarchical style control and minimizing mutual correlation. Besides, we develop an attribute compatibility-aware attention network to deeply explore compatible interactions of attributes and visualize their internal decisions. The empirical experiments and user study on the FashionVC and Polyvore datasets reveal that the decoupling accuracy of PFNet for multiple clothing attributes is increased by an average of 22% compared to the state-of-the-art method, and provides more popular compatibility insights with an accuracy rate of 75.5%.

Attention-Based Personalized Compatibility Learning for Fashion Matching

The fashion industry has a critical need for fashion compatibility. Modeling compatibility is a challenging task that involves extracting (in)compatible features of pairs, obtaining compatible relationships between matching items, and applying them to personalized recommendation tasks. Measuring compatibility is a complex and subjective concept in general. The complexity is reflected in the fact that relationships between fashion items are determined by multiple matching rules, such as color, shape, and material. Each personal aesthetic style and fashion preference differs, adding subjectivity to the compatibility concept. As a result, personalized factors must be considered. Previous works mainly utilize a convolutional neural network to measure compatibility by extracting general features, but they ignore fine-grained compatibility features and only model overall compatibility. We propose a novel neural network framework called the Attention-based Personalized Compatibility Embedding Network (PCE-Net). It comprises two components: attention-based compatibility embedding modeling and attention-based personal preference modeling. In the second part, we utilize matrix factorization and content-based features to obtain user preferences. Both pieces are jointly trained using the BPR framework in an end-to-end method. Extensive experiments on the IQON3000 dataset demonstrate that PCE-Net significantly outperforms most baseline methods.

MCCP: multi-modal fashion compatibility and conditional preference model for personalized clothing recommendation

MCCP: multi-modal fashion compatibility and conditional preference model for personalized clothing recommendation

Body Shape-Aware Object-Level Outfit Completion for Full-Body Portrait Images

Modeling fashion compatibility between different categories of items and forming personalized outfits have become important topics in recommender systems recently. However, item compatibility and outfit recommendation have been explored in perfect settings in the past, where high-quality images of items from the front view or user profiles are available. In this paper, we propose a new task called Complete The full-body Portrait (CTP) for real-world fashion images (e.g., street photos and selfies), which is able to recommend the most compatible item for a masked scene where the outfit is incomplete. Visual compatibility and personalization are the key points for accurate scene-based recommendations. In our approach, the former is accomplished by calculating the visual distance of the query scene and target item in latent space, while the latter is achieved by taking the body-shape information of the human subject into consideration. To obtain side information to train our model, ResNet-50, YOLOv3 and SMPLify-X models are adopted to extract visual features, detect item objects, and reconstruct a 3D body mesh, respectively. Our approach first predicts the missing item category from the masked scene, and then finds the most compatible items from the predicted category through computing visual distances at image level, region level and object level, together with measuring human body-shape compatibility. We conduct extensive experiments on two real-world datasets, Street2Shop and STL-Fashion. Both quantitative and qualitative results show that our model outperforms all baseline models.

Towards private stylists via personalized compatibility learning

Personalized outfit compatibility learning is an emerging yet challenging task. Most of the existing methods focus on general outfit compatibility learning. Although a few works have been proposed for personalized fashion compatibility, they either considered user preference on fashion items with specific patterns or design elements or recommended outfits based on the overall visual similarity according to the users’ preferred collections. This paper adopts physical and fashion attributes for effective personalized fashion compatibility evaluation and recommendation. The physical attributes are concluded into seven aspects: body shape, skin color, hairstyle, hair color, height, breast size (breasts), and color contrast. The personalized outfit compatibility problem in this paper is a multi-label classification problem and formulated as an optimization function with outfit images, fashion attributes, and physical attributes as input. It is the first attempt to solve the problem by discovering the correlation between visual image features, fashion attributes, and physical attributes. Specifically, the correlation is learned with two transformer encoders by updating attention weights of different embedding pairs during the training process. The model can not only predict the fashion attributes of the outfit’s top, bottom, shoes, and bag items, but also predict the incompatible physical attributes of an individual towards the given outfit. It can be used to recommend outfits that best fit an individual and the predicted fashion attributes can be used for result explanation. The O4U dataset, which contains rich annotations of fashion item attributes and human physical attributes of the outfits, is used to evaluate the performance of the proposed method. The quantitative and qualitative results show that the proposed method outperforms state-of-the-art methods for personalized outfit compatibility evaluation.

Fashion clothing matching by global-local feature optimization

目的 由于现有时尚服饰搭配方法缺乏服饰图像局部细节的有效特征表示，难以对不同服饰间的局部兼容性进行建模，限制了服饰兼容性学习的完备性，导致时尚服饰搭配的准确率较低。因此，提出一种全局—局部特征优化的时尚服饰搭配方法。方法 首先，利用不同卷积网络提取时尚服饰的图像和文本特征作为全局特征，同时在卷积网络基础上构建局部特征提取网络，提取时尚服饰图像的局部特征；然后，基于图网络和自注意力机制构建全局—局部兼容性学习模块，通过学习不同时尚服饰全局特征间和局部特征间的交互关系，并定义不同时尚服饰的权重，进行服饰全局和局部兼容性建模；最后，构建服饰搭配优化模型，通过融合套装中所有服饰的全局和局部兼容性优化服饰搭配，并计算搭配得分，输出正确的服饰搭配结果。结果 在公开数据集Polyvore上将本文方法与其他方法进行对比。实验结果表明，利用局部特征提取网络提取的时尚服饰图像局部特征能有效地表示服饰局部信息；构建的全局—局部兼容性学习模块对时尚服饰的全局兼容性和局部兼容性进行了完整建模；构建的时尚服饰搭配优化模型实现了全局和局部兼容性的优化组合，使时尚服饰搭配准确率（fill in the blank，FITB）提高至86.89%。结论 本文提出的全局—局部特征优化的时尚服饰搭配方法，能够有效提高时尚服饰搭配的准确率，较好地满足日常时尚搭配的需求。;Objective Fashion clothing matching has been developing for clothing-relevant fashion research nowadays. Fashion clothing matching studies are required to learn the complex matching relationship（i. e. ，fashion compatibility） among different fashion items in an representation-based outfit. Fashion items have rich partial designs and matching relationships among partial designs. To analyze their global compatibility learning，most of the existing researches are concerned of items’global features（visual and textual features）. But，local feature extraction is often ignored for local compatibility，which causes lower performance and accuracy of fashion style matching. Therefore，we develop a fashion style matching method in terms of global-local feature optimization and it is aimed to extract the local features of fashion images for local information representing，construct the local compatibility of fashion items，and improve the global and local compatibility-incorporated accuracy of fashion style matching. Method First，we use two different convolutional neural networks（CNNs）to extract the global features of fashion items separately on the basis of the input fashion images and texts. To extract CNN-based local features of fashion images，a multiple branches-related local feature extraction network is designed. A branch of the local feature extraction network is composed of 1）a convolution layer，2）a batch normalization （BN）layer，and 3）a rectified linear unit（ReLU）activation function. A branch can be used to extract a local feature in the fashion image，and different branches can be used to extract different local features of the fashion image. Second，a global-local compatibility learning module is constructed in terms of graph neural network（GNN）and self-attention mechanism（SAM），which can model both of the global and local compatibility. GNN is used to model interactions among global features and local features separately. The SAM-based weight information of different fashion items is defined and integrated into the modeling，and the item’s global and local compatibility are obtained both. Finally，a fashion clothing matching optimization model is built up to gain optimized matching results. The learned outfit global and local compatibility can be used to integrate all fashion items’global compatibility and local compatibility separately in an outfit. To optimize matching results，the trade-off parameters are then defined to adjust the impact of the outfit global compatibility and local compatibility on fashion style matching. At the same time，the matching score is calculated as well. Different matching schemes have different matching scores，and the optimized fashion style matching result is generated according to the highest score. Result The proposed method is validated on the public Polyvore dataset that includes fashion item images and textual descriptions. The details are presented as follows. The local features of fashion items extracted by our local feature extraction network can represent the fashion items’local information effectively without label-attributed supervision. Our global-local compatibility learning module can be used to learn the fashion item’s global compatibility and local compatibility at the same time，and the weights of different fashion items is involved，which can model the fashion global and local compatibility completely. The fill in the blank（FITB）accuracy ratio of fashion style matching is improved to 86. 89%. Conclusion A fashion clothing matching method is developed in terms of global local feature optimization. First，we construct a local feature extraction network to extract local features of fashion images while the global features of fashion items are extracted. Next，the self-attention mechanism is introduced to weight different fashion items after the global matching relationships and local matching relationships of fashion items with graph network are analyzed，which constructs the global and local compatibilities of fashion items completely. Finally，to obtain the global and local compatibilities of the outfit， our fashion clothing matching optimization model is used to fuse the item’s global compatibility and local compatibility each in an outfit. To optimize matching results，the effectiveness of the two kinds of compatibilities on fashion clothing matching with parameters is adjusted as well. The convergence speed of our method is still slow. The optimization model is only used to combine the global and local compatibility of the outfit linearly. In practice，the relationship between the global compatibility and local compatibility is more complex. To improve the accuracy of fashion clothing matching，future work can be focused on the convergence speed and the clothing matching optimization further.

Category-Aware Multimodal Attention Network for Fashion Compatibility Modeling

Fashion compatibility modeling, which is used to estimate the matching degree of a given set of fashion items, has received increasing attention in recent years. However, existing studies often fail to fully leverage multimodal information or ignore the semantic guidance of clothing categories in elevating the reliability of multimodal information. In this paper, we propose a fashion compatibility modeling approach with a category-aware multimodal attention network, termed as FCM-CMAN. In FCM-CMAN, we focus on enriching and aggregating multimodal representations of fashion items by means of the dynamic representations of categories and a contextual attention mechanism simultaneously. Specifically, considering that category correlations are always dynamic and varied for different fashion items, we design a categorical dynamic graph convolutional network to adaptively learn the semantic correlations between categories. When combined with the multi-layered visual outputs of a convolutional neural network and the surrounding contextual information, multiple content-aware category representations and context-aware attention weights are obtained to better characterize fashion items from different aspects. On this basis, two pieces of aware information are integrated by a multimodal factorized bilinear pooling strategy to generate visual-semantic embeddings, which are further improved by a multi-head self-attention mechanism to capture significant elements related to fashion compatibility. Extensive experiments conducted on the FashionVC and ExpFashion datasets demonstrate the superiority of FCM-CMAN over state-of-the-art methods.

Learning Fashion Compatibility With Context Conditioning Embedding

Fashion compatibility predictions have obtained a lot of attention recently. Mining the compatibility between fashion items in an outfit is different from learning the visual similarity, since this relationship is more delicate. Decomposing the outfit compatibility into pairwise item matching is a popular way to treat the problem. However, in most existing methods, the items are matched without considering the context, i.e, the remaining items in the outfit. Recent efforts have been made to learn the underlying high order relationships among items by treating the outfit as a whole. These models could be sensitive to the properties of different datasets, and the item representations in these models are not as compact as those in the pairwise models. In this paper, we propose a context conditioning embedding approach to learn compact representations that preserve the shared information among items under the existence of contextual items. We use two different spaces, the general and the contextual spaces, to embed items, where the representation in the contextual space contains information from the context. We employ mutual information maximization for model learning, which is shown to be more appropriate for the problem. With extensive experiments, we show that our model achieves superior performance than other state-of-the-art methods.

COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by Silhouette Masks and Fashion Styles

How to recommend outfits has gained considerable attention in both academia and industry in recent years. Many studies have been carried out regarding fashion compatibility learning, to determine whether the fashion items in an outfit are compatible or not. These methods mainly focus on evaluating the compatibility of existing outfits and rarely consider applying such knowledge to ‘design’ new fashion items. We propose the new task of generating complementary and compatible fashion items based on an arbitrary number of given fashion items. In particular, given some fashion items that can make up an outfit, the aim of this paper is to synthesize photo-realistic images of other, complementary, fashion items that are compatible with the given ones. To achieve this, we propose an outfit generation framework, referred to as COutfitGAN, which includes a pyramid style extractor, an outfit generator, a UNet-based real/fake discriminator, and a collocation discriminator. To train and evaluate this framework, we collected a large-scale fashion outfit dataset with over 200K outfits and 800K fashion items from the Internet. Extensive experiments show that COutfitGAN outperforms other baselines in terms of similarity, authenticity, and compatibility measurements.

Neural stylist: Towards online styling service

Online stylist service enjoys huge economic potentials due to the trend of transformation of the fashion industry to digitalisation. Existing works either predict the fashion compatibility from the overall aspect or evaluate the compatibility with type-conditional representations. The prediction is hard to interpret due to the abstractive forecast. This paper proposes a visual and semantic representation model for explainable evaluation and recommendation. The model considers fashion compatibility from different factors, such as colour, material and style, by leveraging low to high-level features from former to later layers of CNN. The colour correlation and the pairwise relationship of fashion items in the same outfit are considered during the prediction stage. Instead of just predicting an outfit as compatible or incompatible, the model can classify an outfit as three precise evaluation levels: Good, Normal and Bad. The detailed compatible level is more consistent with the fashion sense of our human brain as Good or Bad outfits may have specific characteristics while Normal outfits tend to be ordinary. Additionally, the model can diagnose and recommend substitutions of the problematic fashion items from overall compatibility or colour-specific aspects by tracking the prediction matrices’ backpropagation gradients during the recommendation stage. Experiments in terms of outfit compatibility prediction and fill in the blank are conducted to evaluate the prediction ability of the proposed model. In contrast, fashion substitution recommendation experiments are conducted to assess the compatibility diagnosis and recommendation ability. Quantitative and qualitative results show that the model enables online stylist services with excellent explainability and generalisation on fashion prediction and recommendation.

Attribute-aware heterogeneous graph network for fashion compatibility prediction

Fashion compatibility prediction aims to provide a compatibility score for a set of fashion combinations, making an effort to meet people’s needs for clothing matching in daily life. One difficulty of this problem is that whether the fashion items are compatible depends on a variety of attribute factors, such as design style, material, color. Existing works mostly use the attributes as the item feature representations or integrate attribute features into the compatibility modeling process as auxiliary information in a simple way. These works ignore the fine-grained attributes that can play a role as bridges in establishing the potential compatibility relationship between fashion items. To better take advantage of fashion attributes information, we propose an Attribute-aware Heterogeneous Graph Network (AHGN), which combines the fashion items and their attributes as a heterogeneous graph to integrate the heterogeneous information in the fashion compatibility problem. While fusing the fine-grained attribute information into the item nodes representation, the attributes act as bridges to transfer the characteristics of potential compatible fashion items into the target item node. Previous methods of calculating outfit score by item pairs without considering the effect of outfit compositions. An outfit-based item pairs scoring method is proposed. The feature representation of item pairs is adjusted by integrating the overall outfit information to consider the influence of the outfit environment on the item pairs. We conduct experiments on three tasks (fill-in-the-blank, compatibility prediction, and fashion item retrieval) in two real-world fashion datasets: Fashion32 and IQON3000. Experimental results demonstrate the effectiveness of our proposed method.