Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos

By Yannis Kalantidis, L. Kennedy, Li-Jia Li. 2013

199 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Recommender Systems

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Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, methodology): “Another method [16] uses segmentation to detect clothing classes in the query image before it retrieves visually similar products from each of the detected classes.”
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, methodology): “Another method was proposed by Kalantidis, Kennedy, and Li (2013), which accepts a query image and uses segmentation to detect clothing classes before retrieving visually similar products from each of the detected classes.”
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.”
Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark (2018, background): “COMPREHENSIVE human visual understanding of scenarios in the wild, which is regarded as one of the most fundamental problems in computer vision, could have a crucial impact in many higher-level application domains, such as person re-identification [1], video surveillance [2], human behavior analysis”
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.”
Where to Buy It: Matching Street Clothing Photos in Online Shops (2015, background): “There have also been some efforts on cross-scenario retrieval [24, 23, 9, 16].”
Runway to Realway: Visual Analysis of Fashion (2015, methodology): “Our trend analyses could also enable new fashion applications, for example, by improving market analysis of fashion brands [13], interactive search [14] or recommendation [12, 17] of trending styles.”
MMALFM (2018, methodology): “ions to the visual appearances of items in recommender systems, especially for the items of which the visual appearances are crucial side-signals, such as clothing [25, 28, 46]. For example, [34] and [36] consider visual features specifically for the task of clothing recommendation. Their methods ar”
Hipster Wars: Discovering Elements of Fashion Styles (2014, background): “Recent papers have looked at the problem of parsing clothing (predicting pixel-wise labelings of garment items) [35,36] and clothing recognition for applications [22,23,11].”
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].”
Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network (2015, methodology): “Due to the huge impact for e-commerce applications, there is a growing interest in methods for clothing retrieval [20, 33, 31, 44] and outfit recommendation [18].”