Hipster Wars: Discovering Elements of Fashion Styles

By M. Kiapour, Kota Yamaguchi, Alexander C. Berg, Tamara L. Berg. 2014

254 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Image-Based Recommendations on Styles and Substitutes (2015, background): “The system shares these characteristics with more common forms of recommender system, but does so on the basis of the appearance of the object, rather than metadata, reviews, or similar.”
Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network (2015, methodology): “In particular, clothing recognition has been used for contextaided people identification [13], fashion style recognition [21], occupation recognition [39], and social tribe prediction [26].”
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations (2016, background): “Recently, extensive research efforts have been devoted to clothes classification [11, 1, 29], attribute prediction [3, 13, 4, 24], and clothing item retrieval [17, 6, 10, 27, 15], because of their potential values to the industry.”
Automatic Spatially-Aware Fashion Concept Discovery (2017, background): “Recently, researchers have annotated clothes with semantic attributes [9, 2, 8, 16, 11] (e.”
Deep domain adaptation for describing people based on fine-grained clothing attributes (2015, background): “Extracting clothing attributes for analysis of fashion images is another topic that has recently attracted interest [3, 45, 24, 33].”
Deep Learning the City: Quantifying Urban Perception at a Global Scale (2016, methodology): “In the ﬁrst step, these methods [11,40,47,48] convert the pairwise comparisons to ranked scores for images using the Microsoft TrueSkill [49] algorithm.”
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization (2017, background): “Fullysupervised convolutional networks (CNNs) have demonstrated great performance on object detection [16, 15, 30], segmentation [31] and attribute localization [11, 60, 27], but require expensive human annotations for training (e.”
Fashion Landmark Detection in the Wild (2016, background): “Recent advances include predicting semantic attributes [16,8,9,17,3], clothes recognition and retrieval [4,18,19,20,2,3] and fashion trends discovery [6,21,7].”
Where to Buy It: Matching Street Clothing Photos in Online Shops (2015, background): “Other works have explored ways to identify aspects of a person’s socio-identity, including predicting their social tribe [19, 17], fashionability [36, 30], or occupation [31] from the clothing they are wearing.”
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback (2021, background): “Many fashion datasets have been proposed over the past few years, covering different applications such as fashionability and style prediction [50, 28, 22, 51], fashion image generation [46], product search and recommendation [25, 72, 19, 41, 63], fashion apparel pixelwise segmentation [27, 74, 69], ”