Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Computational Technologies for Fashion Recommendation: A Survey

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this paper, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyse its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from fashion recommendation technologies. the computational technologies of fashion recommendation

On the Design of Sales Support Systems for Online Apparel Stores

Many online stores apply several sales support systems, e.g., recommender systems, sorting and filtering tools, to support buyers during the shopping process. Although, the research highlights the positive effect of such systems, the current study questions its applicability in online stores for products which serve users\u27 needs to be unique like apparel or luxury products. We analyze female users\u27 buying behavior of apparel products in a laboratory setting and find that users with high trendiness undertake in general more search steps. Further, we find that most users rely during their search process on different sorting and filtering as well as on keyword search tools while personalized and non-personalized recommendations play a minor role for users in this industry. Further, we find that users with high trendiness avoid following top seller lists and wear with it -recommendations. Moreover, the provision of top seller rankings does not influence the consumers\u27 product choice

Explainable online recommendation systems with self-identity theory and attribute learning method

In recent years, Online Shopping plays an important role in daily life and how to improve the online shopping experience with Machine Learning and Recommender System has been discussed by a group of researchers. As a sub-field of Machine Learning, Computer vision has achieved significant developments during the last decade. The computer vision techniques can help machine to view images and extract useful information from images like human beings. However, the existing online recommender system has mostly used the labelled information and ignored the large amount of useful information extracted from images. This thesis proposed that the extracted information from images through computer vision techniques can be used in the current online recommender system for the improved online shopping experience. To do this, I firstly tackled the problem of insufficient data in the real online shopping environment. I proposed a pairwise constraint random forest algorithm with associating transfer learning strategy. This new algorithm can make use of weakly supervised labelled data which is relatively easy to collect in the real online shopping environment to train the attribute classification model. Secondly, I developed an explainable recommender system with self-identity theory. This new recommender framework is built based on the weakly learning algorithm proposed above to analyse human behaviours by self-identity theory from information system research. Compared with previous recommender system, my work concentrates on different customer behaviours distinguished by self-identity and result in an improved online shopping experience. In summary, there are two major contributes for this thesis. Firstly, this thesis introduces a new weakly-supervised learning approach for semantic data classification in the online shopping environment. This new algorithm can work with noise partially labelled data to achieve better accuracy for attribute learning tasks. Secondly, by analysing the recommender system with self-identity theory, a new explainable Recommender System is proposed to improve online shopping experience. Besides, we also indicate the potential of further research in combining Computer Vision in Computer Science with online shopping experience in Information System research which can determine how Computer Vision can help to solve real world problems

Perceptual MAP of Online Fashion Store

The main theme of this research is Fashion Marketing and focus on customers-driven marketing strategy and brand image marketing strategy. The purpose of this research is to map customers-based positioning of local online fashion companies, which are benchmarked by global leading fashion brands. In this research, multidimensional scaling is being applied to process the data. Multidimensional scaling is purposed to map the ordinal data and determine its spatial configuration based on the similarities and dissimilarities. The data applied in this research are obtained from 60 respondents in 600 cases with 10 brands as its objects. According to Tucker’s Coefficient of Congruence, the factors in the questionnaire are virtually identical but nevertheless, the questionnaire is good based on the normalized stress testKeywords: Consumer, Fashion, Local, Online, Marketing, Strategy, Targe

신체 임베딩을 활용한 오토인코더 기반 컴퓨터 비전 모형의 성능 개선

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2021.8. 박종헌.Deep learning models have dominated the field of computer vision, achieving state-of-the-art performance in various tasks. In particular, with recent increases in images and videos of people being posted on social media, research on computer vision tasks for analyzing human visual information is being used in various ways. This thesis addresses classifying fashion styles and measuring motion similarity as two computer vision tasks related to humans. In real-world fashion style classification problems, the number of samples collected for each style class varies according to the fashion trend at the time of data collection, resulting in class imbalance. In this thesis, to cope with this class imbalance problem, generalized few-shot learning, in which both minority classes and majority classes are used for learning and evaluation, is employed. Additionally, the modalities of the foreground images, cropped to show only the body and fashion item parts, and the fashion attribute information are reflected in the fashion image embedding through a variational autoencoder. The K-fashion dataset collected from a Korean fashion shopping mall is used for the model training and evaluation. Motion similarity measurement is used as a sub-module in various tasks such as action recognition, anomaly detection, and person re-identification; however, it has attracted less attention than the other tasks because the same motion can be represented differently depending on the performer's body structure and camera angle. The lack of public datasets for model training and evaluation also makes research challenging. Therefore, we propose an artificial dataset for model training, with motion embeddings separated from the body structure and camera angle attributes for training using an autoencoder architecture. The autoencoder is designed to generate motion embeddings for each body part to measure motion similarity by body part. Furthermore, motion speed is synchronized by matching patches performing similar motions using dynamic time warping. The similarity score dataset for evaluation was collected through a crowdsourcing platform utilizing videos of NTU RGB+D 120, a dataset for action recognition. When the proposed models were verified with each evaluation dataset, both outperformed the baselines. In the fashion style classification problem, the proposed model showed the most balanced performance, without bias toward either the minority classes or the majority classes, among all the models. In addition, In the motion similarity measurement experiments, the correlation coefficient of the proposed model to the human-measured similarity score was higher than that of the baselines.컴퓨터 비전은 딥러닝 학습 방법론이 강점을 보이는 분야로, 다양한 태스크에서 우수한 성능을 보이고 있다. 특히, 사람이 포함된 이미지나 동영상을 딥러닝을 통해 분석하는 태스크의 경우, 최근 소셜 미디어에 사람이 포함된 이미지 또는 동영상 게시물이 늘어나면서 그 활용 가치가 높아지고 있다. 본 논문에서는 사람과 관련된 컴퓨터 비전 태스크 중 패션 스타일 분류 문제와 동작 유사도 측정에 대해 다룬다. 패션 스타일 분류 문제의 경우, 데이터 수집 시점의 패션 유행에 따라 스타일 클래스별 수집되는 샘플의 양이 달라지기 때문에 이로부터 클래스 불균형이 발생한다. 본 논문에서는 이러한 클래스 불균형 문제에 대처하기 위하여, 소수 샘플 클래스와 다수 샘플 클래스를 학습 및 평가에 모두 사용하는 일반화된 퓨샷러닝으로 패션 스타일 분류 문제를 설정하였다. 또한 변분 오토인코더 기반의 모델을 통해, 신체 및 패션 아이템 부분만 잘라낸 전경 이미지 모달리티와 패션 속성 정보 모달리티가 패션 이미지의 임베딩 학습에 반영되도록 하였다. 학습 및 평가를 위한 데이터셋으로는 한국 패션 쇼핑몰에서 수집된 K-fashion 데이터셋을 사용하였다. 한편, 동작 유사도 측정은 행위 인식, 이상 동작 감지, 사람 재인식 같은 다양한 분야의 하위 모듈로 활용되고 있지만 그 자체가 연구된 적은 많지 않은데, 이는 같은 동작을 수행하더라도 신체 구조 및 카메라 각도에 따라 다르게 표현될 수 있다는 점으로 부터 기인한다. 학습 및 평가를 위한 공개 데이터셋이 많지 않다는 점 또한 연구를 어렵게 하는 요인이다. 따라서 본 논문에서는 학습을 위한 인공 데이터셋을 수집하여 오토인코더 구조를 통해 신체 구조 및 카메라 각도 요소가 분리된 동작 임베딩을 학습하였다. 이때, 각 신체 부위별로 동작 임베딩을 생성할 수 있도록하여 신체 부위별로 동작 유사도 측정이 가능하도록 하였다. 두 동작 사이의 유사도를 측정할 때에는 동적 시간 워핑 기법을 사용, 비슷한 동작을 수행하는 구간끼리 정렬시켜 유사도를 측정하도록 함으로써, 동작 수행 속도의 차이를 보정하였다. 평가를 위한 유사도 점수 데이터셋은 행위 인식 데이터셋인 NTU-RGB+D 120의 영상을 활용하여 크라우드 소싱 플랫폼을 통해 수집되었다. 두 가지 태스크의 제안 모델을 각각의 평가 데이터셋으로 검증한 결과, 모두 비교 모델 대비 우수한 성능을 기록하였다. 패션 스타일 분류 문제의 경우, 모든 비교군에서 소수 샘플 클래스와 다수 샘플 클래스 중 한 쪽으로 치우치지 않는 가장 균형잡힌 추론 성능을 보여주었고, 동작 유사도 측정의 경우 사람이 측정한 유사도 점수와 상관계수에서 비교 모델 대비 더 높은 수치를 나타내었다.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Research contribution 5 1.2.1 Fashion style classication 5 1.2.2 Human motion similarity 9 1.2.3 Summary of the contributions 11 1.3 Thesis outline 13 Chapter 2 Literature Review 14 2.1 Fashion style classication 14 2.1.1 Machine learning and deep learning-based approaches 14 2.1.2 Class imbalance 15 2.1.3 Variational autoencoder 17 2.2 Human motion similarity 19 2.2.1 Measuring the similarity between two people 19 2.2.2 Human body embedding 20 2.2.3 Datasets for measuring the similarity 20 2.2.4 Triplet and quadruplet losses 21 2.2.5 Dynamic time warping 22 Chapter 3 Fashion Style Classication 24 3.1 Dataset for fashion style classication: K-fashion 24 3.2 Multimodal variational inference for fashion style classication 28 3.2.1 CADA-VAE 31 3.2.2 Generating multimodal features 33 3.2.3 Classier training with cyclic oversampling 36 3.3 Experimental results for fashion style classication 38 3.3.1 Implementation details 38 3.3.2 Settings for experiments 42 3.3.3 Experimental results on K-fashion 44 3.3.4 Qualitative analysis 48 3.3.5 Eectiveness of the cyclic oversampling 50 Chapter 4 Motion Similarity Measurement 53 4.1 Datasets for motion similarity 53 4.1.1 Synthetic motion dataset: SARA dataset 53 4.1.2 NTU RGB+D 120 similarity annotations 55 4.2 Framework for measuring motion similarity 58 4.2.1 Body part embedding model 58 4.2.2 Measuring motion similarity 67 4.3 Experimental results for measuring motion similarity 68 4.3.1 Implementation details 68 4.3.2 Experimental results on NTU RGB+D 120 similarity annotations 72 4.3.3 Visualization of motion latent clusters 78 4.4 Application 81 4.4.1 Real-world application with dancing videos 81 4.4.2 Tuning similarity scores to match human perception 87 Chapter 5 Conclusions 89 5.1 Summary and contributions 89 5.2 Limitations and future research 91 Appendices 93 Chapter A NTU RGB+D 120 Similarity Annotations 94 A.1 Data collection 94 A.2 AMT score analysis 95 Chapter B Data Cleansing of NTU RGB+D 120 Skeletal Data 100 Chapter C Motion Sequence Generation Using Mixamo 102 Bibliography 104 국문초록 123박