Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the unexplainable characteristic makes such deep learning based approach cannot meet the the designer, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized fashion outfit compositions, we propose a partitioned embedding network to learn interpretable representations from clothing items. The overall network architecture consists of three components: an auto-encoder module, a supervised attributes module and a multi-independent module. The auto-encoder module serves to encode all useful information into the embedding. In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the multi-independent module, adversarial operation are adopted to fulfill the mutually independent constraint. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Given specified attributes description, our model can recommend a ranked list of outfit composition with interpretable matching scores. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.
Fashion Outfit Composition. As described above, due to the difficulty in modeling outfit composition, there are few works @cite_23 @cite_25 @cite_13 studying fashion outfit composition. Iwata . @cite_23 propose a topic model to recommend "Tops" for "Bottoms". The goal of this work is to compose fashion outfit automatically. challenging in modeling many aspects of the fashion outfits, such as compatibility and aesthetics. Veit . @cite_19 use a Siamese Convolutional Neural Network (CNN) architecture to learn clothing matching from the Amazon co-purchase dataset, focusing on the representative problem of learning compatible clothing style. Simo-Serra1 . @cite_13 introduce a Conditional Random Field (CRF) to learn the different outfits, types of people and settings. The model is used to predict how fashionable a person looks on a particular photograph. Li . @cite_25 use the quality scores as the label and multi-modal embeddings as features to train a grading model. The framework of Li's approach is shown in Figure (a). However, the mixed multi model embedding used by Li's model is unexplainable, which leads to unexplainable outfit composition.
@cite_19 @cite_13 @cite_25 @cite_23
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This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29 88.23 segmentation accuracy and 65.52 63.89 recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods. Nowadays, as a beauty-enhancing product, clothing plays an important role in human's social life. In fact, the key to a proper outfit usually lies in the harmonious clothing matching. Nevertheless, not everyone is good at clothing matching. Fortunately, with the proliferation of fashion-oriented online communities, fashion experts can publicly share their fashion tips by showcasing their outfit compositions, where each fashion item (e.g., a top or bottom) usually has an image and context metadata (e.g., title and category). Such rich fashion data offer us a new opportunity to investigate the code in clothing matching. However, challenges co-exist with opportunities. The first challenge lies in the complicated factors, such as color, material and shape, that affect the compatibility of fashion items. Second, as each fashion item involves multiple modalities (i.e., image and text), how to cope with the heterogeneous multi-modal data also poses a great challenge. Third, our pilot study shows that the composition relation between fashion items is rather sparse, which makes traditional matrix factorization methods not applicable. Towards this end, in this work, we propose a content-based neural scheme to model the compatibility between fashion items based on the Bayesian personalized ranking (BPR) framework. The scheme is able to jointly model the coherent relation between modalities of items and their implicit matching preference. Experiments verify the effectiveness of our scheme, and we deliver deep insights that can benefit future research. This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as “image cosegmentation,” iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1] . In the second phase (i.e., “region colabeling”), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [2] , we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29 88.23 segmentation accuracy and 65.52 63.89 recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results. We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose lighting background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed inbetween the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes.