Fashion Meets Computer Vision: A Survey

ACM Comput. Surv., 2021.

Cited by: 0|Views112
EI
Weibo:
The enormous amount of data generated by social media platforms and e-commerce websites provide an opportunity to explore knowledge relevant to support the development of intelligent fashion techniques

Abstract:

Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related ...More

Code:

Data:

0
Full Text
Bibtex
Weibo
Introduction
  • Fashion is how the authors present themselves to the world. The way the authors dress and makeup defines the unique style and distinguishes them from other people.
  • Fashion is how the authors present themselves to the world.
  • Revenue in the Fashion segment amounts to over US $718 billion in 2020 and is expected to present an annual growth of 8.4%2.
  • As the revolution of computer vision with artificial intelligence (AI) is underway, AI is starting to hit the magnanimous field of fashion, whereby reshaping the fashion life with a wide range of application innovations from electronic retailing, personalized stylist, to the fashion design process.
  • The authors term the computer-vision-enabled fashion technology as intelligent fashion.
  • Intelligent fashion is a challenging task because, unlike generic objects, fashion items suffer from significant variations in style and design, and, most importantly, the long-standing semantic gap between computable low-level features and high-level semantic concepts that they encode is huge
Highlights
  • Fashion is how we present ourselves to the world
  • – – – – – – 80.14 81.76 85.51 37.09 51.92 58.12 clothing retrieval since there is a large domain discrepancy between daily human photo captured in general environment and clothing images taken in ideal conditions
  • A prior probability map of the human body was obtained through pose estimation to guide clothing segmentation, and the segments were classified through locality-sensitive hashing
  • In addition to connecting with the customers with the use of artificial intelligence (AI) chatbots, we identify there are four other ways that AI is transforming the future of fashion and beauty, which include: (1) Improving product discovery
  • The enormous amount of data generated by social media platforms and e-commerce websites provide an opportunity to explore knowledge relevant to support the development of intelligent fashion techniques
  • The global fashion apparel market alone has surpassed 3 trillion US dollars today, and accounts for nearly 2 percent of the world’s Gross Domestic Product (GDP)1
  • Arising from the above, there has much computer vision (CV)-based fashion technology been proposed to handle the problems of fashion image detection, analysis, synthesis, recommendation, and its applications
Methods
  • DFA [120] DLAN [199] AttentiveNet [176] Global-Local [92] FLD [120]

    DFA [120] DLAN [199] AttentiveNet [176] Global-Local [92]

    ‘L. Collar’ represents left collar, while ‘R. Collar’ represents right collar. “–” represents detailed results are not available.

    0.0680 0.0672 0.0583 0.0568 retrieval, which covered most significant fashion detection works.
  • 0.0680 0.0672 0.0583 0.0568 retrieval, which covered most significant fashion detection works.
  • There are four benchmark datasets for fashion landmark detection, and the most used is Fashion Landmark Dataset [120].
  • These datasets differ in two major aspects: (1) standardization process of the images, and (2) pose and scale variations.
  • The normalized error (NE), which is defined as the l2 distance between detected and the ground truth landmarks in the normalized coordinate space, is the most popular evaluation metric used in fashion landmark detection benchmarks.
  • AMNet [211] FashionSearchNet [1] AMNet [211] FashionSearchNet [1]
Results
  • Method [102, 201].
  • Yamaguchi et al [196] CCP [102, 201] Yamaguchi et al [196] CCP [102, 201] [104] ATR Fashionista.
  • Yamaguchi et al [195, 197] Liang et al [103] Co-CNN [104] Yamaguchi et al [195, 197] Liang et al [103] Co-CNN [104] [178] LIP.
  • Liu et al [118] proposed to utilize an unsupervised transfer learning method based on part-based alignment and features derived from the sparse reconstruction.
  • Kalantidis et al [82] presented clothing retrieval from the perspective of human parsing.
Conclusion
  • With the significant advancement of information technology, research in computer vision (CV) and its applications in fashion have become a hot topic and received a great deal of attention.
  • The enormous amount of data generated by social media platforms and e-commerce websites provide an opportunity to explore knowledge relevant to support the development of intelligent fashion techniques.
  • Arising from the above, there has much CV-based fashion technology been proposed to handle the problems of fashion image detection, analysis, synthesis, recommendation, and its applications.
  • Despite recent progress, investigating and modeling complex real-world problems when developing intelligent fashion solutions remain challenging.
  • Given the enormous profit potential in the ever-growing consumer fashion and beauty industry, the studies on intelligent fashion-related tasks will continue to grow and expand
Summary
  • Introduction

    Fashion is how the authors present themselves to the world. The way the authors dress and makeup defines the unique style and distinguishes them from other people.
  • Fashion is how the authors present themselves to the world.
  • Revenue in the Fashion segment amounts to over US $718 billion in 2020 and is expected to present an annual growth of 8.4%2.
  • As the revolution of computer vision with artificial intelligence (AI) is underway, AI is starting to hit the magnanimous field of fashion, whereby reshaping the fashion life with a wide range of application innovations from electronic retailing, personalized stylist, to the fashion design process.
  • The authors term the computer-vision-enabled fashion technology as intelligent fashion.
  • Intelligent fashion is a challenging task because, unlike generic objects, fashion items suffer from significant variations in style and design, and, most importantly, the long-standing semantic gap between computable low-level features and high-level semantic concepts that they encode is huge
  • Methods

    DFA [120] DLAN [199] AttentiveNet [176] Global-Local [92] FLD [120]

    DFA [120] DLAN [199] AttentiveNet [176] Global-Local [92]

    ‘L. Collar’ represents left collar, while ‘R. Collar’ represents right collar. “–” represents detailed results are not available.

    0.0680 0.0672 0.0583 0.0568 retrieval, which covered most significant fashion detection works.
  • 0.0680 0.0672 0.0583 0.0568 retrieval, which covered most significant fashion detection works.
  • There are four benchmark datasets for fashion landmark detection, and the most used is Fashion Landmark Dataset [120].
  • These datasets differ in two major aspects: (1) standardization process of the images, and (2) pose and scale variations.
  • The normalized error (NE), which is defined as the l2 distance between detected and the ground truth landmarks in the normalized coordinate space, is the most popular evaluation metric used in fashion landmark detection benchmarks.
  • AMNet [211] FashionSearchNet [1] AMNet [211] FashionSearchNet [1]
  • Results

    Method [102, 201].
  • Yamaguchi et al [196] CCP [102, 201] Yamaguchi et al [196] CCP [102, 201] [104] ATR Fashionista.
  • Yamaguchi et al [195, 197] Liang et al [103] Co-CNN [104] Yamaguchi et al [195, 197] Liang et al [103] Co-CNN [104] [178] LIP.
  • Liu et al [118] proposed to utilize an unsupervised transfer learning method based on part-based alignment and features derived from the sparse reconstruction.
  • Kalantidis et al [82] presented clothing retrieval from the perspective of human parsing.
  • Conclusion

    With the significant advancement of information technology, research in computer vision (CV) and its applications in fashion have become a hot topic and received a great deal of attention.
  • The enormous amount of data generated by social media platforms and e-commerce websites provide an opportunity to explore knowledge relevant to support the development of intelligent fashion techniques.
  • Arising from the above, there has much CV-based fashion technology been proposed to handle the problems of fashion image detection, analysis, synthesis, recommendation, and its applications.
  • Despite recent progress, investigating and modeling complex real-world problems when developing intelligent fashion solutions remain challenging.
  • Given the enormous profit potential in the ever-growing consumer fashion and beauty industry, the studies on intelligent fashion-related tasks will continue to grow and expand
Tables
  • Table1: Summary of the benchmark datasets for fashion landmark detection task
  • Table2: Performance comparisons of fashion landmark detection methods in terms of normalized error (NE)
  • Table3: Summary of the benchmark datasets for fashion parsing task
  • Table4: Performance comparisons of fashion parsing methods (in %)
  • Table5: Summary of the benchmark datasets for fashion retrieval task
  • Table6: Performance comparisons of some clothing retrieval methods
  • Table7: Summary of the benchmark datasets for clothing attribute recognition task
  • Table8: Performance comparisons of attribute recognition methods in terms of top-k classification accuracy
  • Table9: Summary of the benchmark datasets for style learning task
  • Table10: Summary of the benchmark datasets for popularity prediction task
  • Table11: Summary of the benchmark datasets for style transfer task
  • Table12: Summary of the benchmark datasets for pose transformation task
  • Table13: Summary of the benchmark datasets for fashion compatibility task
  • Table14: Summary of the benchmark datasets for outfit matching task
  • Table15: Summary of the benchmark datasets for hairstyle suggestion task
Download tables as Excel
Funding
  • The global fashion apparel market alone has surpassed 3 trillion US dollars today, and accounts for nearly 2 percent of the world’s Gross Domestic Product (GDP)1
Study subjects and analysis
benchmark datasets: 4
They built a strong model Match R-CNN based on Mask R-CNN [52] for solving the four tasks.

2.1.2 Benchmark datasets. As summarized in Table 1, there are four benchmark datasets for fashion landmark detection, and the most used is Fashion Landmark Dataset [120]. These datasets differ in two major aspects: (1) standardization process of the images, and (2) pose and scale variations.

2.1.3 Performance evaluations

benchmark datasets: 4
2.1.2 Benchmark datasets. As summarized in Table 1, there are four benchmark datasets for fashion landmark detection, and the most used is Fashion Landmark Dataset [120]. These datasets differ in two major aspects: (1) standardization process of the images, and (2) pose and scale variations

subjects: 1001
# of photos 2,000 1,275 2,002. 1000 non-makeup faces and 1000 reference faces Images in 3 sub-regions (eye, mouth, and skin); labeled with procedures of makeup 1,001 subjects, where each subject has a pair of photos being with and without makeup 961 different females (224 Caucasian, 187 Asian, 300 African, and 250 Hispanic) where one with clean face and another after professional makeup; annotated with facial attributes. 1,148 non-makeup images and 1,044 makeup images

datasets: 3
Besides, there is one dataset collected in videos [5]. All of the three datasets are summarized in Table 12. 4.2.3 Performance evaluations

users: 3568
Annotated with clothing categories and celebrity names Categories includes top, bottom, shoes, bag, and accessory Categories contains top, bottom, shoes, accessory, dress and tunic, and coat. The outfits within this dataset was created by 3,568 users. Sources Polyvore.com Polyvore.com Ranker.com, fashion magazine sites Bodymeasurements.org

users with his: 11784
Annotated with independent and ready for wearing (off-body module) or dependent (on-body module), or a bounding box. Annotated with categories of shoes, tops, pants, handbags, coats, sunglasses, shorts, skirts, earrings, and necklaces It contains 11,784 users with his/her latest historical purchase records in total of 116,532 user-item records It contains 797 users with 262 outfits and each outfit with 2 items, i.e., a top and a bottom. It is composed of 583,000 individual items

Reference
  • K. E. Ak, A. A. Kassim, J. H. Lim, and J. Y. Tham. 2018. Learning Attribute Representations With Localization for Flexible Fashion Search. In CVPR.
    Google ScholarFindings
  • Z. Al-Halah, R. Stiefelhagen, and K. Grauman. 2017. Fashion Forward: Forecasting Visual Style in Fashion. In ICCV.
    Google ScholarFindings
  • T. Alashkar, S. Jiang, S., and Y. Fu. 2017. Examples-Rules Guided Deep Neural Network for Makeup Recommendation. In AAAI.
    Google ScholarFindings
  • K. Ayush, S. Jandial, A. Chopra, and B. Krishnamurthy. 2019. Powering Virtual Try-On via Auxiliary Human Segmentation Learning. In ICCVW.
    Google ScholarFindings
  • G. Balakrishnan, A. Zhao, A. V. Dalca, F. Durand, and J. Guttag. 2018. Synthesizing Images of Humans in Unseen Poses. In CVPR.
    Google ScholarFindings
  • L. Banica, D. Pirvu, and A. Hagiu. 2014. Neural Networks Based Forecasting for Romanian Clothing Sector. In IFFS.
    Google ScholarFindings
  • F. C. Heilbron, B. Pepik, Z. Barzelay, and M. Donoser. 2019. Clothing Recognition in the Wild using the Amazon Catalog. In ICCVW.
    Google ScholarFindings
  • M. Chai, T. Shao, H. Wu, Y. Weng, and K. Zhou. 2016. AutoHair: Fully Automatic Hair Modeling from A Single Image. ACM TOG (2016).
    Google ScholarLocate open access versionFindings
  • H. Chang, J. Lu, F. Yu, and A. Finkelstein. 2018. PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup. In CVPR.
    Google ScholarFindings
  • Y. Chang, W. Cheng, B. Wu, and K. Hua. 2017. Fashion World Map: Understanding Cities Through Streetwear Fashion. In ACM MM.
    Google ScholarLocate open access versionFindings
  • Y. Chang, M. Chuang, S. Hung, S. Shen, and B. Chu. 2003. A Kansei Study on the Style Image of Fashion Design. Asian Design Conference (2003).
    Google ScholarLocate open access versionFindings
  • F. Chen and D. Zhang. 2016. Combining a Causal Effect Criterion for Evaluation of Facial Attractiveness Models. Neurocomputing (2016).
    Google ScholarLocate open access versionFindings
  • H. Chen, A. Gallagher, and B. Girod. 2012. Describing clothing by semantic attributes. In ECCV.
    Google ScholarFindings
  • H. Chen, K. Hui, S. Wang, L. Tsao, H. Shuai, and W. Cheng. 2019. BeautyGlow: On-Demand Makeup Transfer Framework with Reversible
    Google ScholarFindings
  • I. Chen and C. Lu. 2017. Sales forecasting by combining clustering and machine-learning techniques for computer retailing. NEURAL COMPUT
    Google ScholarFindings
  • K. Chen, K. Chen, P. Cong, H. Hsu, and J. Luo. 2015. Who are the Devils Wearing Prada in New York City?. In ACM MM.
    Google ScholarLocate open access versionFindings
  • K. Chen and J. Luo. 20When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features. In WWW.
    Google ScholarFindings
  • L. Chen and Y. He. 20Dress Fashionably: Learn Fashion Collocation With Deep Mixed-Category Metric Learning. In AAAI.
    Google ScholarFindings
  • L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2018. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI (2018).
    Google ScholarLocate open access versionFindings
  • Q. Chen, J. Huang, R. Feris, L. M Brown, J. Dong, and S. Yan. 2015. Deep domain adaptation for describing people based on fine-grained clothing attributes. In CVPR.
    Google ScholarFindings
  • Q. Chen, G. Wang, and C. L. Tan. 2013. Modeling fashion. In ICME.
    Google ScholarFindings
  • W. Chen, P. Huang, J. Xu, X. Guo, C. Guo, F. Sun, C. Li, A. Pfadler, H. Zhao, and B. Zhao. 2019. POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba IFashion. In ACM SIGKDD.
    Google ScholarLocate open access versionFindings
  • W. Cheng, J. Jia, S. Liu, J. Fu, J. Liu, S. C. Hidayati, J. Tseng, and J. Huang. 2019. Perfect Corp. Challenge 2019: Half Million Beauty Product Image Recognition. https://challenge2019.perfectcorp.com/.
    Findings
  • Z. Cheng, X. Wu, Y. Liu, and X. Hua. 2017. Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images. In CVPR.
    Google ScholarFindings
  • T. Choi, C. Hui, S. Ng, and Y. Yu. 2012. Color Trend Forecasting of Fashionable Products with Very Few Historical Data. IEEE T SYST MAN CY C
    Google ScholarLocate open access versionFindings
  • C. Corbiere, H. Ben-Younes, A. Ramé, and C. Ollion. 2017. Leveraging weakly annotated data for fashion image retrieval and label prediction. In
    Google ScholarLocate open access versionFindings
  • J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In CVPR.
    Google ScholarFindings
  • J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, T. Darrell, and K. Saenko. 2015. Long-term recurrent convolutional networks for visual recognition and description. In CVPR.
    Google ScholarFindings
  • H. Dong, X. Liang, X. Shen, B. Wang, H. Lai, J. Zhu, Z. Hu, and J. Yin. 2019. Towards Multi-Pose Guided Virtual Try-On Network. In ICCV.
    Google ScholarFindings
  • H. Dong, X. Liang, X. Shen, B. Wu, B. Chen, and J. Yin. 2019. FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On. In ICCV.
    Google ScholarFindings
  • J. Dong, Q. Chen, X. Shen, J. Yang, and S. Yan. 2014. Towards Unified Human Parsing and Pose Estimation. In CVPR.
    Google ScholarFindings
  • J. Dong, Q. Chen, W. Xia, Z. Huang, and S. Yan. 2013. A deformable mixture parsing model with parselets. In ICCV.
    Google ScholarFindings
  • X. Dong, X. Song, F. Feng, P. Jing, X. Xu, and L. Nie. 2019. Personalized Capsule Wardrobe Creation with Garment and User Modeling. In ACM
    Google ScholarLocate open access versionFindings
  • P. Esser, E. Sutter, and B. Ommer. 2018. A Variational U-Net for Conditional Appearance and Shape Generation. In CVPR.
    Google ScholarFindings
  • Y. Fei, H. T. Maia, C. Batty, C. Zheng, and E. Grinspun. 2017. A Multi-scale Model for Simulating Liquid-hair Interactions. ACM TOG (2017).
    Google ScholarLocate open access versionFindings
  • L. Gao, W. Li, Z. Huang, D. Huang, and Y. Wang. 2018. Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning. In ICPR.
    Google ScholarFindings
  • N. Garcia and G. Vogiatzis. 2017. Dress like a Star: Retrieving Fashion Products from Videos. In ICCVW.
    Google ScholarLocate open access versionFindings
  • Y. Ge, R. Zhang, L. Wu, X. Wang, X. Tang, and P. Luo. 2019. A Versatile Benchmark for Detection, Pose Estimation, Segmentation and
    Google ScholarFindings
  • K. Gong, Y. Gao, X. Liang, X. Shen, M. Wang, and L. Lin. 2019. Graphonomy: Universal Human Parsing via Graph Transfer Learning. In CVPR.
    Google ScholarFindings
  • K. Gong, X. Liang, Y. Li, Y. Chen, M. Yang, and L. Lin. 2018. Instance-level Human Parsing via Part Grouping Network. In ECCV.
    Google ScholarFindings
  • K. Gong, X. Liang, D. Zhang, X. Shen, and L. Lin. 2017. Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for
    Google ScholarFindings
  • Q. Gu, G. Wang, M. T. Chiu, Y. Tai, and C. Tang. 2019. LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup. In
    Google ScholarLocate open access versionFindings
  • X. Gu, Y. Wong, P. Peng, L. Shou, G. Chen, and M. S. Kankanhalli. 2017. Understanding Fashion Trends from Street Photos via Neighbor-Constrained
    Google ScholarFindings
  • P. Guan, L. Reiss, D. A. Hirshberg, A. Weiss, and M. J. Black. 2012. DRAPE: DRessing Any PErson. ACM TOG (2012).
    Google ScholarLocate open access versionFindings
  • M. H. Kiapour, X. Han, S. Lazebnik, A. C. Berg, and T. L. Berg. 2015. Where to buy it: Matching street clothing photos in online shops. In ICCV.
    Google ScholarFindings
  • Y. Ha, S. Kwon, M. Cha, and J. Joo. 2017. Fashion Conversation Data on Instagram. In ICWSM.
    Google ScholarFindings
  • X. Han, X. Hu, W. Huang, and M. R. Scott. 2019. ClothFlow: A Flow-Based Model for Clothed Person Generation. In ICCV.
    Google ScholarFindings
  • X. Han, Z. Wu, P. X. Huang, X. Zhang, M. Zhu, Y. Li, Y. Zhao, and L. S. Davis. 2017. Automatic Spatially-Aware Fashion Concept Discovery. In
    Google ScholarLocate open access versionFindings
  • X. Han, Z. Wu, W. Huang, M. R. Scott, and L. S. Davis. 2019. Compatible and Diverse Fashion Image Inpainting. In ICCV.
    Google ScholarLocate open access versionFindings
  • X. Han, Z. Wu, Y. Jiang, and L. S. Davis. 2017. Learning Fashion Compatibility with Bidirectional LSTMs. In ACM MM.
    Google ScholarLocate open access versionFindings
  • X. Han, Z. Wu, Z. Wu, R. Yu, and L. S Davis. 2018. VITON: An image-based virtual try-on network. In CVPR.
    Google ScholarFindings
  • Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. 2017. Mask R-CNN. In ICCV.
    Google ScholarLocate open access versionFindings
  • R. He and J. McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW.
    Google ScholarFindings
  • R. He and J. McAuley. 2016. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In AAAI.
    Google ScholarFindings
  • S. C. Hidayati, Y. Chen, C. Yang, and K. Hua. 2017. Popularity Meter: An Influence- and Aesthetics-aware Social Media Popularity Predictor. In
    Google ScholarLocate open access versionFindings
  • S. C. Hidayati, W. Cheng, and K. Hua. 2012. Clothing Genre Classification by Exploiting the Style Elements. In ACM MM.
    Google ScholarLocate open access versionFindings
  • S. C. Hidayati, T. W. Goh, J. Gary Chan, C. Hsu, J. See, L. Wong, K. Hua, Y. Tsao, and W. Cheng. 2020. Dress with Style: Learning Style from Joint Deep Embedding of Clothing Styles and Body Shapes. IEEE TMM (2020).
    Google ScholarLocate open access versionFindings
  • S. C. Hidayati, C. Hsu, Y. Chang, K. Hua, J. Fu, and W. Cheng. 2018. What Dress Fits Me Best?: Fashion Recommendation on the Clothing Style for
    Google ScholarFindings
  • S. C. Hidayati, K. Hua, W. Cheng, and S. Sun. 2014. What Are the Fashion Trends in New York?. In ACM MM.
    Google ScholarLocate open access versionFindings
  • S. C. Hidayati, K. Hua, Y. Tsao, H. Shuai, J. Liu, and W. Cheng. 2019. Garment Detectives: Discovering Clothes and Its Genre in Consumer Photos. In MIPR.
    Google ScholarFindings
  • S. C. Hidayati, C. You, W. Cheng, and K. Hua. 2018. Learning and Recognition of Clothing Genres From Full-Body Images. IEEE Trans Cybern
    Google ScholarFindings
  • M. Hou, L. Wu, E. Chen, Z. Li, V. W. Zheng, and Q. Liu. 2019. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach. In IJCAI.
    Google ScholarFindings
  • W. Hsiao and K. Grauman. 2017. Learning the latent “look”: Unsupervised discovery of a style-coherent embedding from fashion images. In ICCV.
    Google ScholarFindings
  • W. Hsiao and K. Grauman. 2018. Creating Capsule Wardrobes from Fashion Images. In CVPR.
    Google ScholarFindings
  • W. Hsiao, I. Katsman, C. Wu, D. Parikh, and K. Grauman. 2019. Fashion++: Minimal Edits for Outfit Improvement. In ICCV.
    Google ScholarFindings
  • C. Hsieh, C. Chen, C. Chou, H. Shuai, and W. Cheng. 2019. Fit-me: Image-Based Virtual Try-on With Arbitrary Poses. In ICIP.
    Google ScholarFindings
  • C. Hsieh, C. Chen, C. Chou, H. Shuai, J. Liu, and W. Cheng. 2019. FashionOn: Semantic-Guided Image-Based Virtual Try-on with Detailed Human and Clothing Information. In ACM MM.
    Google ScholarLocate open access versionFindings
  • Y. Hu, X. Yi, and L. S. Davis. 2015. Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach. In ACM MM.
    Google ScholarLocate open access versionFindings
  • J. Huang, R. S. Feris, Q. Chen, and S. Yan. 2015. Cross-domain image retrieval with a dual attribute-aware ranking network. In ICCV.
    Google ScholarFindings
  • J. Huang, W. Xia, and S. Yan. 2014. Deep Search with Attribute-aware Deep Network. In ACM MM.
    Google ScholarLocate open access versionFindings
  • C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu. 2014. Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments. TPAMI (2014).
    Google ScholarLocate open access versionFindings
  • P. Isola, J. Zhu, T. Zhou, and A. A. Efros. 2017. Image-to-Image Translation with Conditional Adversarial Networks. In CVPR.
    Google ScholarFindings
  • T. Iwata, S. Watanabe, and H. Sawada. 2011. Fashion Coordinates Recommender System using Photographs from Fashion Magazines. In IJCAI.
    Google ScholarFindings
  • H. J. Lee, R. Lee, M. Kang, M. Cho, and G. Park. 2019. LA-VITON: A Network for Looking-Attractive Virtual Try-On. In ICCVW.
    Google ScholarFindings
  • N. Jetchev and U. Bergmann. 2017. The Conditional Analogy GAN: Swapping Fashion Articles on People Images. In ICCVW.
    Google ScholarFindings
  • W. Ji, X. Li, Y. Zhuang, O. El Farouk Bourahla, Y. Ji, S. Li, and J. Cui. 2018. Semantic Locality-aware Deformable Network for Clothing
    Google ScholarFindings
  • J. Jia, J. Huang, G. Shen, T. He, Z. Liu, H. Luan, and C. Yan. 2016. Learning to appreciate the aesthetic effects of clothing. In AAAI.
    Google ScholarFindings
  • S. Jiang and Y. Fu. 2017. Fashion style generator. In IJCAI.
    Google ScholarFindings
  • S. Jiang, M. Shao, C. Jia, and Y. Fu. 2016. Consensus Style Centralizing Auto-Encoder for Weak Style Classification. In AAAI.
    Google ScholarFindings
  • S. Jiang, Y. Wu, and Y. Fu. 2016. Deep Bi-directional Cross-triplet Embedding for Cross-Domain Clothing Retrieval. In ACM MM.
    Google ScholarLocate open access versionFindings
  • S. Jiang, Y. Wu, and Y. Fu. 2018. Deep Bidirectional Cross-Triplet Embedding for Online Clothing Shopping. ACM TOMM (2018).
    Google ScholarLocate open access versionFindings
  • Y. Kalantidis, L. Kennedy, and L. Li. 2013. Getting the Look: Clothing Recognition and Segmentation for Automatic Product Suggestions in
    Google ScholarFindings
  • W. Kang, E. Kim, J. Leskovec, C. Rosenberg, and J. McAuley. 2019. Complete the Look: Scene-based Complementary Product Recommendation. In
    Google ScholarLocate open access versionFindings
  • M. Kaya, E. Yesil, M. F. Dodurka, and S. Sıradag. 2014. Fuzzy Forecast Combining for Apparel Demand Forecasting. In IFFS.
    Google ScholarFindings
  • M. H. Kiapour, K. Yamaguchi, A. C. Berg, and T. L. Berg. 2014. Hipster Wars: Discovering Elements of Fashion Styles. In ECCV.
    Google ScholarFindings
  • A. Kovashka, D. Parikh, and K. Grauman. 2012. WhittleSearch: Image Search with Relative Attribute Feedback. In CVPR.
    Google ScholarFindings
  • Z. Kuang, Y. Gao, G. Li, P. Luo, Y. Chen, L. Lin, and W. Q. Zhang. 2019. Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid. In ICCV.
    Google ScholarFindings
  • K. Laenen, S. Zoghbi, and M. Moens. 2018. Web Search of Fashion Items with Multimodal Querying. In ACM WSDM.
    Google ScholarLocate open access versionFindings
  • Z. Lähner, D. Cremers, and T. Tung. 2018. DeepWrinkles: Accurate and Realistic Clothing Modeling. In ECCV.
    Google ScholarFindings
  • C. Lassner, G. Pons-Moll, and P. V Gehler. 2017. A Generative Model of People in Clothing. In ICCV.
    Google ScholarFindings
  • A. Laurentini and A. Bottino. 2014. Computer analysis of face beauty: A survey. CVIU (2014).
    Google ScholarLocate open access versionFindings
  • S. Lee, S. Oh, C. Jung, and C. Kim. 2019. A Global-Local Emebdding Module for Fashion Landmark Detection. In ICCVW.
    Google ScholarFindings
  • C. Li, K. Zhou, and S. Lin. 2015. Simulating makeup through physics-based manipulation of intrinsic image layers. In CVPR.
    Google ScholarFindings
  • J. Li, J. Zhao, Y. Wei, C. Lang, Y. Li, T. Sim, S. Yan, and J. Feng. 2017. Multi-Human Parsing in the Wild. arXiv.
    Google ScholarFindings
  • T. Li, R. Qian, C. Dong, S. Liu, Q. Yan, W. Zhu, and L. Lin. 2018. BeautyGAN: Instance-level facial makeup transfer with deep generative adversarial network. In ACM MM.
    Google ScholarLocate open access versionFindings
  • Y. Li, L. Cao, J. Zhu, and J. Luo. 2017. Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data. IEEE TMM
    Google ScholarFindings
  • Y. Li, C. Huang, C. C. Loy, and X. Tang. 2016. Human Attribute Recognition by Deep Hierarchical Contexts. In ECCV.
    Google ScholarFindings
  • Y. Li, L. Song, X. Wu, R. He, and T. Tan. 2018. Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification. In
    Google ScholarLocate open access versionFindings
  • Z. Li, Y. Li, W. Tian, Y. Pang, and Y. Liu. 2016. Cross-scenario clothing retrieval and fine-grained style recognition. In ICPR.
    Google ScholarFindings
  • L. Liang, L. Lin, L. Jin, D. Xie, and M. Li. 2018. SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction. In
    Google ScholarLocate open access versionFindings
  • X. Liang, K. Gong, X. Shen, and L. Lin. 2018. Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark. TPAMI
    Google ScholarLocate open access versionFindings
  • X. Liang, L. Lin, W. Yang, P. Luo, J. Huang, and S. Yan. 2016. Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval. IEEE TMM (2016).
    Google ScholarFindings
  • X. Liang, S. Liu, X. Shen, J. Yang, L. Liu, J. Dong, L. Lin, and S. Yan. 2015. Deep Human Parsing with Active Template Regression. TPAMI (2015).
    Google ScholarLocate open access versionFindings
  • X. Liang, C. Xu, X. Shen, J. Yang, S. Liu, J. Tang, L. Lin, and S. Yan. 2015. Human Parsing with Contextualized Convolutional Neural Network. In ICCV.
    Google ScholarLocate open access versionFindings
  • L. Liao, X. He, B. Zhao, C. Ngo, and T. Chua. 2018. Interpretable Multimodal Retrieval for Fashion Products. In ACM MM.
    Google ScholarLocate open access versionFindings
  • L. Lin, L. Liang, L. Jin, and W. Chen. 2019. Attribute-Aware Convolutional Neural Networks for Facial Beauty Prediction. In IJCAI.
    Google ScholarFindings
  • T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. 2014. Microsoft COCO: Common Objects in Context. In
    Google ScholarLocate open access versionFindings
  • Z. Lin, H. Xie, P. Kang, Z. Yang, W. Liu, and Q. Li. 2019. Cross-Domain Beauty Item Retrieval via Unsupervised Embedding Learning. In ACM
    Google ScholarLocate open access versionFindings
  • K. Liu, T. Chen, and C. Chen. 2016. MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction. In ICMR.
    Google ScholarFindings
  • L. Liu, J. Xing, S. Liu, H. Xu, X. Zhou, and S. Yan. 2014. Wow! You Are So Beautiful Today! ACM TOMM (2014).
    Google ScholarLocate open access versionFindings
  • S. Liu, Y. Fan, A. Samal, and Z. Guo. 2016. Advances in computational facial attractiveness methods. Multimedia Tools and Applications (2016).
    Google ScholarLocate open access versionFindings
  • S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, and S. Yan. 2014. Fashion Parsing With Weak Color-Category Labels. IEEE TMM (2014).
    Google ScholarLocate open access versionFindings
  • S. Liu, J. Feng, Z. Song, T. Zhang, H. Lu, C. Xu, and S. Yan. 2012.
    Google ScholarFindings
  • S. Liu, X. Liang, L. Liu, K. Lu, L. Lin, and S. Yan. 2014. Fashion Parsing with Video Context. In ACM MM.
    Google ScholarLocate open access versionFindings
  • S. Liu, X. Liang, L. Liu, X. Shen, J. Yang, C. Xu, L. Lin, X. Cao, and S. Yan. 2015. Matching-CNN Meets KNN: Quasi-Parametric Human Parsing. In CVPR.
    Google ScholarFindings
  • S. Liu, L. Liu, and S. Yan. 2014. Fashion Analysis: Current Techniques and Future Directions. IEEE MultiMedia (2014).
    Google ScholarLocate open access versionFindings
  • S. Liu, X. Ou, R. Qian, W. Wang, and X. Cao. 2016. Makeup Like a Superstar: Deep Localized Makeup Transfer Network. In IJCAI.
    Google ScholarFindings
  • S. Liu, Z. Song, G. Liu, C. Xu, H. Lu, and S. Yan. 2012. Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set. In
    Google ScholarLocate open access versionFindings
  • Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang. 2016. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In CVPR.
    Google ScholarFindings
  • Z. Liu, S. Yan, P. Luo, X. Wang, and X. Tang. 2016. Fashion landmark detection in the wild. In ECCV.
    Google ScholarLocate open access versionFindings
  • L. Lo, C. Liu, R. Lin, B. Wu, H. Shuai, and W. Cheng. 2019. Dressing for Attention: Outfit Based Fashion Popularity Prediction. In ICIP.
    Google ScholarFindings
  • B. Loni, L. Y. Cheung, M. Riegler, A. Bozzon, L. Gottlieb, and M. Larson. 2014. Fashion 10000: An Enriched Social Image Dataset for Fashion and
    Google ScholarFindings
  • X. Luo, Z. Su, J. Guo, G. Zhang, and X. He. 2018. Trusted Guidance Pyramid Network for Human Parsing. In ACM MM.
    Google ScholarLocate open access versionFindings
  • L. Ma, X. Jia, Q. Sun, B. Schiele, T. Tuytelaars, and L. V. Gool. 2017. Pose guided person image generation. In NIPS.
    Google ScholarFindings
  • L. Ma, Q. Sun, S. Georgoulis, L. V. Gool, B. Schiele, and M. Fritz. 2018. Disentangled person image generation. In CVPR.
    Google ScholarFindings
  • Y. Ma, J. Jia, S. Zhou, J. Fu, Y. Liu, and Z. Tong. 2017. Towards better understanding the clothing fashion styles: A multimodal deep learning approach. In AAAI.
    Google ScholarFindings
  • Y. Ma, X. Yang, L. Liao, Y. Cao, and T. Chua. 2019.
    Google ScholarFindings
  • Z. Ma, J. Dong, Z. Long, Y. Zhang, Y. He, H. Xue, and S. Ji. 2020. Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding
    Google ScholarFindings
  • U. Mall, K. Matzen, B. Hariharan, N. Snavely, and K. Bala. 2019. GeoStyle: Discovering fashion trends and events. In ICCV.
    Google ScholarFindings
  • E. Massip, S. C. Hidayati, W. Cheng, and K. Hua. 2018. Exploiting Category-Specific Information for Image Popularity Prediction in Social Media. In ICMEW.
    Google ScholarLocate open access versionFindings
  • K. Matzen, K. Bala, and N. Snavely. 2017. StreetStyle: Exploring world-wide clothing styles from millions of photos. In arXiv.
    Google ScholarFindings
  • J. McAuley, C. Targett, Q. Shi, and A. van den Hengel. 2015. Image-Based Recommendations on Styles and Substitutes. In ACM SIGIR.
    Google ScholarLocate open access versionFindings
  • J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. 2011. Multimodal deep learning. In ICML.
    Google ScholarFindings
  • T. V. Nguyen, S. Liu, B. Ni, J. Tan, Y. Rui, and S. Yan. 2012. Sense Beauty via Face, Dressing, and/or Voice. In ACM MM.
    Google ScholarLocate open access versionFindings
  • Y. Ni and F. Fan. 2011. A two-stage dynamic sales forecasting model for the fashion retail. Expert Syst. Appl. (2011).
    Google ScholarLocate open access versionFindings
  • X. Nie, J. Feng, and S. Yan. 2018. Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation. In ECCV.
    Google ScholarFindings
  • S. Paris, H. M. Briceño, and F. X. Sillion. 2004. Capture of Hair Geometry from Multiple Images. ACM TOG (2004).
    Google ScholarLocate open access versionFindings
  • J. Park, G. L. Ciampaglia, and E. Ferrara. 2016. Style in the age of Instagram: Predicting success within the fashion industry using social media. In
    Google ScholarLocate open access versionFindings
  • G. Pons-Moll, S. Pujades, S. Hu, and M. J. Black. 2017. ClothCap: Seamless 4D Clothing Capture and Retargeting. ACM TOG (2017).
    Google ScholarLocate open access versionFindings
  • A. Pumarola, A. Agudo, A. Sanfeliu, and F. Moreno-Noguer. 2018. Unsupervised Person Image Synthesis in Arbitrary Poses. In CVPR.
    Google ScholarLocate open access versionFindings
  • B. Q. Ferreira, J. P. Costeira, R. G. Sousa, L. Gui, and J. P. Gomes. 2019. Pose Guided Attention for Multi-Label Fashion Image Classification. In
    Google ScholarLocate open access versionFindings
  • S. Qin, S. Kim, and R. Manduchi. 2017. Automatic skin and hair masking using fully convolutional networks. In ICME.
    Google ScholarFindings
  • S. Ren, T. Choi, and N. Liu. 2015. Fashion Sales Forecasting with a Panel Data-Based Particle-Filter Model. IEEE T SYST MAN CY A (2015).
    Google ScholarFindings
  • R. Rothe, R. Timofte, and L. V. Gool. 2016. Some like it hot - visual guidance for preference prediction. In CVPR.
    Google ScholarFindings
  • C. Rousset and P. Coulon. 2008. Frequential and color analysis for hair mask segmentation. In ICIP.
    Google ScholarFindings
  • T. Ruan, T. Liu, Z. Huang, Y. Wei, S. Wei, Y. Zhao, and T. Huang. 2019. Devil in the Details: Towards Accurate Single and Multiple Human Parsing.
    Google ScholarFindings
  • J. Sanchez-Riera, J. Lin, K. Hua, W. Cheng, and A. W. Tsui. 2017. i-Stylist: Finding the Right Dress Through Your Social Networks. In MMM.
    Google ScholarFindings
  • I. Santesteban, M. A. Otaduy, and D. Casas. 2019. Learning-Based Animation of Clothing for Virtual Try-On. Comput. Graph. Forum (2019).
    Google ScholarLocate open access versionFindings
  • A. Selle, M. Lentine, and R. Fedkiw. 2008. A Mass Spring Model for Hair Simulation. ACM TOG (2008).
    Google ScholarLocate open access versionFindings
  • S. Shi, F. Gao, X. Meng, X. Xu, and J. Zhu. 2019. Improving Facial Attractiveness Prediction via Co-attention Learning. In ICASSP.
    Google ScholarFindings
  • T. Shi, Y. Yuan, C. Fan, Z. Zou, Z. Shi, and Y. Liu. 2019. Face-to-Parameter Translation for Game Character Auto-Creation. In ICCV.
    Google ScholarFindings
  • Y. Shih, K. Chang, H. Lin, and M. Sun. 2018. Compatibility family learning for item recommendation and generation. In AAAI.
    Google ScholarFindings
  • C. Si, W. Wang, L. Wang, and T. Tan. 2018. Multistage Adversarial Losses for Pose-Based Human Image Synthesis. In CVPR.
    Google ScholarFindings
  • A. Siarohin, E. Sangineto, S. Lathuilière, and N. Sebe. 2018. Deformable GANs for pose-based human image generation. In CVPR.
    Google ScholarFindings
  • E. Simo-Serra, S. Fidler, F. Moreno-Noguer, and R. Urtasun. 2015. Neuroaesthetics in fashion: Modeling the perception of fashionability. In CVPR.
    Google ScholarFindings
  • E. Simo-Serra and H. Ishikawa. 2016. Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction. In
    Google ScholarLocate open access versionFindings
  • S. Song and T. Mei. 2018. When Multimedia Meets Fashion. IEEE MultiMedia (2018).
    Google ScholarLocate open access versionFindings
  • S. Song, W. Zhang, J. Liu, and T. Mei. 2019. Unsupervised Person Image Generation with Semantic Parsing Transformation. In CVPR.
    Google ScholarFindings
  • X. Song, F. Feng, X. Han, X. Yang, W. Liu, and L. Nie. 2018. Neural Compatibility Modeling with Attentive Knowledge Distillation. In ACM SIGIR.
    Google ScholarLocate open access versionFindings
  • X. Song, F. Feng, J. Liu, Z. Li, L. Nie, and J. Ma. 2017. Neurostylist: Neural compatibility modeling for clothing matching. In ACM MM.
    Google ScholarLocate open access versionFindings
  • X. Song, X. Han, Y. Li, J. Chen, X. Xu, and L. Nie. 2019. GP-BPR: Personalized Compatibility Modeling for Clothing Matching. In ACM MM.
    Google ScholarLocate open access versionFindings
  • G. Sun, X. Wu, and Q. Peng. 2016. Part-based clothing image annotation by visual neighbor retrieval. Neurocomputing (2016).
    Google ScholarLocate open access versionFindings
  • K. Vaccaro, S. Shivakumar, Z. Ding, K. Karahalios, and R. Kumar. 2016. The Elements of Fashion Style. In ACM UIST.
    Google ScholarLocate open access versionFindings
  • M. I. Vasileva, B. A. Plummer, K. Dusad, S. Rajpal, R. Kumar, and D. Forsyth. 2018. Learning Type-Aware Embeddings for Fashion Compatibility. In ECCV.
    Google ScholarFindings
  • A. Veit, S. Belongie, and T. Karaletsos. 2017. Conditional Similarity Networks. In CVPR.
    Google ScholarLocate open access versionFindings
  • A. Veit, B. Kovacs, S. Bell, J. McAuley, K. Bala, and S. Belongie. 2015. Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences. In ICCV.
    Google ScholarFindings
  • S. Vittayakorn, A. C. Berg, and T. L. Berg. 2017. When was that made?. In WACV.
    Google ScholarLocate open access versionFindings
  • S. Vittayakorn, T. Umeda, K. Murasaki, K. Sudo, T. Okatani, and K. Yamaguchi. 2016. Automatic attribute discovery with neural activations. In
    Google ScholarLocate open access versionFindings
  • S. Vittayakorn, K. Yamaguchi, A. C. Berg, and T. L. Berg. 2015. Runway to Realway: Visual Analysis of Fashion. In WACV.
    Google ScholarFindings
  • B. Wang, H. Zheng, X. Liang, Y. Chen, L. Lin, and M. Yang. 2018. Toward characteristic-preserving image-based virtual try-on network. In ECCV.
    Google ScholarFindings
  • H. Wang, J. F. O’Brien, and R. Ramamoorthi. 2011. Data-driven Elastic Models for Cloth: Modeling and Measurement. ACM TOG (2011).
    Google ScholarLocate open access versionFindings
  • J. Wang and J. Allebach. 2015. Automatic assessment of online fashion shopping photo aesthetic quality. In ICIP.
    Google ScholarFindings
  • N. Wang, H. Ai, and S. Lao. 2010. A compositional exemplar-based model for hair segmentation. In ACCV.
    Google ScholarFindings
  • N. Wang, H. Ai, and F. Tang. 2012. What are good parts for hair shape modeling?. In CVPR.
    Google ScholarFindings
  • S. Wang and Y. Fu. 2016. Face Behind Makeup. In AAAI.
    Google ScholarFindings
  • W. Wang, Y. Xu, J. Shen, and S. Zhu. 2018. Attentive fashion grammar network for fashion landmark detection and clothing category classification. In CVPR.
    Google ScholarFindings
  • W. Wang, W. Zhang, J. Wang, J. Yan, and H. Zha. 2018. Learning Sequential Correlation for User Generated Textual Content Popularity Prediction. In IJCAI.
    Google ScholarFindings
  • W. Wang, Z. Zhang, S. Qi, J. Shen, Y. Pang, and L. Shao. 2020. Learning Compositional Neural Information Fusion for Human Parsing. In ICCV.
    Google ScholarFindings
  • X. Wang, Z. Sun, W. Zhang, Y. Zhou, and Y. Jiang. 2016. Matching User Photos to Online Products with Robust Deep Features. In ICMR.
    Google ScholarFindings
  • X. Wang, B. Wu, and Y. Zhong. 2019. Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network. In ACM MM.
    Google ScholarLocate open access versionFindings
  • X. Wang and T. Zhang. 2011. Clothes search in consumer photos via color matching and attribute learning. In ACM MM.
    Google ScholarLocate open access versionFindings
  • Y. Wang, T. Shao, K. Fu, and N. Mitra. 2019. Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation. ACM TOG
    Google ScholarLocate open access versionFindings
  • B. Wu, W. Cheng, P. Liu, Z. Zeng, and J. Luo. 2019. SMP Challenge: An Overview of Social Media Prediction Challenge 2019. In ACM MM.
    Google ScholarLocate open access versionFindings
  • B. Wu, W. Cheng, Y. Zhang, H. Qiushi, L. Jintao, and T. Mei. 2017. Sequential Prediction of Social Media Popularity with Deep Temporal Context
    Google ScholarFindings
  • B. Wu, T. Mei, W. Cheng, and Y. Zhang. 2016. Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal
    Google ScholarFindings
  • Z. Wu, G. Lin, Q. Tao, and J. Cai. 2019. M2E-Try On Net: Fashion from Model to Everyone. In ACM MM.
    Google ScholarLocate open access versionFindings
  • F. Xia, P. Wang, X. Chen, and A. L. Yuille. 2017. Joint Multi-Person Pose Estimation and Semantic Part Segmentation. In CVPR.
    Google ScholarFindings
  • W. Xian, P. Sangkloy, V. Agrawal, A. Raj, J. Lu, C. Fang, F. Yu, and J. Hays. 2018. TextureGAN: Controlling Deep Image Synthesis with Texture
    Google ScholarFindings
  • D. Xie, L. Liang, L. Jin, J. Xu, and M. Li. 2015. SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception. In ICPR.
    Google ScholarFindings
  • Y. Xiong, K. Zhu, D. Lin, and X. Tang. 2015. Recognize complex events from static images by fusing deep channels. In CVPR.
    Google ScholarFindings
  • J. Xu, L. Jin, L. Liang, Z. Feng, D. Xie, and H. Mao. 2017. Facial attractiveness prediction using psychologically inspired convolutional neural network (PI-CNN). In ICASSP.
    Google ScholarFindings
  • Z. Xu, H. Wu, L. Wang, C. Zheng, X. Tong, and Y. Qi. 2014. Dynamic Hair Capture Using Spacetime Optimization. ACM TOG (2014).
    Google ScholarLocate open access versionFindings
  • Y. Yacoob and L. S. Davis. 2006. Detection and analysis of hair. TPAMI (2006).
    Google ScholarLocate open access versionFindings
  • K. Yamaguchi, T. L. Berg, and L. E. Ortiz. 2014. Chic or Social: Visual Popularity Analysis in Online Fashion Networks. In ACM MM.
    Google ScholarLocate open access versionFindings
  • K. Yamaguchi, M. H. Kiapour, and T. L. Berg. 2013. Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items. In ICCV.
    Google ScholarFindings
  • K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg. 2012. Parsing Clothing in Fashion Photographs. In CVPR.
    Google ScholarLocate open access versionFindings
  • K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg. 2014. Retrieving Similar Styles to Parse Clothing. TPAMI (2014).
    Google ScholarLocate open access versionFindings
  • K. Yamaguchi, T. Okatani, K. Sudo, K. Murasaki, and Y. Taniguchi. 2015. Mix and Match: Joint Model for Clothing and Attribute Recognition. In
    Google ScholarLocate open access versionFindings
  • S. Yan, Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang. 2017. Unconstrained fashion landmark detection via hierarchical recurrent transformer networks. In ACM MM.
    Google ScholarLocate open access versionFindings
  • S. Yang, T. Ambert, Z. Pan, K. Wang, L. Yu, T. Berg, and M. C. Lin. 2017. Detailed garment recovery from a single-view image. In ICCV.
    Google ScholarFindings
  • W. Yang, P. Luo, and L. Lin. 2014. Clothing co-parsing by joint image segmentation and labeling. In CVPR.
    Google ScholarFindings
  • W. Yang, M. Toyoura, and X. Mao. 2012. Hairstyle suggestion using statistical learning. In MMM.
    Google ScholarFindings
  • X. Yang, Y. Ma, L. Liao, M. Wang, and T. Chua. 2019. TransNFCM: Translation-Based Neural Fashion Compatibility Modeling. In AAAI.
    Google ScholarLocate open access versionFindings
  • W. Yin, Y. Fu, Y. Ma, Y. Jiang, T. Xiang, and X. Xue. 2017. Learning to Generate and Edit Hairstyles. In ACM MM.
    Google ScholarLocate open access versionFindings
  • D. Yoo, N. Kim, S. Park, Anthony S Paek, and In So Kweon. 2016. Pixel-level domain transfer. In ECCV.
    Google ScholarLocate open access versionFindings
  • A. Yu and K. Grauman. 2014. Fine-Grained Visual Comparisons with Local Learning. In CVPR.
    Google ScholarFindings
  • C. Yu, Y. Hu, Y. Chen, and B. Zeng. 2019. Personalized Fashion Design. In ICCV.
    Google ScholarLocate open access versionFindings
  • R. Yu, X. Wang, and X. Xie. 2019. VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature Preservation. In ICCV.
    Google ScholarFindings
  • T. Yu, Z. Zheng, Y. Zhong, J. Zhao, Q. Dai, G. Pons-Moll, and Y. Liu. 2019. SimulCap: Single-View Human Performance Capture With Cloth
    Google ScholarFindings
  • X. Zhang, J. Jia, K. Gao, Y. Zhang, D. Zhang, J. Li, and Q. Tian. 2017. Trip Outfits Advisor: Location-Oriented Clothing Recommendation. IEEE
    Google ScholarFindings
  • B. Zhao, J. Feng, X. Wu, and S. Yan. 2017. Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search. In CVPR.
    Google ScholarFindings
  • J. Zhao, J. Li, Y. Cheng, T. Sim, S. Yan, and J. Feng. 2018. Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A
    Google ScholarFindings
  • L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian. 2015. Scalable Person Re-identification: A Benchmark. In ICCV.
    Google ScholarFindings
  • N. Zheng, X. Song, Z. Chen, L. Hu, D. Cao, and L. Nie. 2019. Virtually Trying on New Clothing with Arbitrary Poses. In ACM MM.
    Google ScholarLocate open access versionFindings
  • S. Zheng, F. Yang, M. H. Kiapour, and R. Piramuthu. 2018. ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations. In ACM MM.
    Google ScholarLocate open access versionFindings
  • Z. Zheng and C. Kambhamettu. 2017. Multi-level Feature Learning for Face Recognition under Makeup Changes. In FG.
    Google ScholarFindings
  • J. Zhu, T. Park, P. Isola, and A. A. Efros. 2017. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. In ICCV.
    Google ScholarFindings
  • S. Zhu, R. Urtasun, S. Fidler, D. Lin, and C. C. Loy. 2017. Be your own Prada: Fashion synthesis with structural coherence. In ICCV.
    Google ScholarFindings
  • X. Zou, X. Kong, W. Wong, C. Wang, Y. Liu, and Y. Cao. 2019. FashionAI: A Hierarchical Dataset for Fashion Understanding. In CVPRW.
    Google ScholarFindings
Your rating :
0

 

Tags
Comments