Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items

By Kota Yamaguchi, M. Kiapour, Tamara L. Berg. 2013

295 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Hypercolumns for object segmentation and fine-grained localization (2014, methodology): “[41, 40] parse fashion photographs into clothing items.”
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.”
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images (2019, background): “With the development of fashion datasets [20, 5, 7, 3, 14, 12, 21, 1], significant progresses have been achieved in this area [2, 19, 17, 18, 9, 8].”
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations (2016, background): “Some other datasets in the vision community were dedicated to the tasks of clothes segmentation, parsing [32, 31, 23, 16, 33] and fashion modeling [24, 30], while DeepFashion focuses on clothes recognition and retrieval.”
Where to Buy It: Matching Street Clothing Photos in Online Shops (2015, background): “Some recent papers have demonstrated effective methods for clothing parsing, where the goal is to assign a semantic clothing label to each pixel in an image of a person [35, 38, 37, 7, 22].”
Deep Human Parsing with Active Template Regression (2015, methodology): “The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing.”
A High Performance CRF Model for Clothes Parsing (2014, methodology): “A set of evaluation metrics and the full source code of approaches [1,13] are provided.”
Runway to Realway: Visual Analysis of Fashion (2015, methodology): “Additionally, we make use of the Paper Doll dataset [26] collected from the Chictopia social network, where people interested in fashion post and annotate pictures of their daily outfits.”
Chic or Social: Visual Popularity Analysis in Online Fashion Networks (2014, methodology): “We compute the parse descriptor in the following steps: 1) Compute clothing parse using [19], and obtain 10 masks corresponding to speciﬁc garment groups, such as outer top , dress , or footware .”
Semantic Object Parsing with Graph LSTM (2016, methodology): “Many higherlevel computer vision applications can benefit from a powerful semantic object parser, including action recognition [3], clothes recognition and retrieval [4] and human behavior analysis [5].”
Fashion Landmark Detection in the Wild (2016, background): “To better capture discriminative information in fashion items, previous works have explored the usage of full image [2], general object proposals [2], bounding boxes [9,17] and even masks [22,18,23,24].”
Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark (2018, background): “Recently, many research efforts have been devoted to human parsing [7], [13], [17],”
VITON: An Image-Based Virtual Try-on Network (2017, methodology): “Most existing methods focus on clothing parsing [44, 28], clothing recognition by attributes [31], matching clothing seen on the street to online products [30, 14], fashion recommendation [19], visual compatibility learning [43, 16], and fashion trend prediction [2].”
Beyond Bounding Boxes: Precise Localization of Objects in Images (2015, background): “This is different from most prior work on these problems [57, 56, 60, 59, 4, 45], which operates on the immediate vicinity of ground-truth”
Instance-level Human Parsing via Part Grouping Network (2018, background): “However, previous approaches only focus on the single-person parsing task in the simpliﬁed and limited conditions, such as fashion pictures [41,8,18,23,6] with upright poses and diverse daily images [11], and disregard more real-world cases where multiple person instances appear in one image.”
Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing (2017, background): “The recent progress on human parsing [5, 28, 31, 30, 9, 25, 19, 17] has been achieved by improving the feature representations using convolutional neural networks and recurrent neural networks.”
Semantic Object Parsing with Local-Global Long Short-Term Memory (2015, methodology): “We use the same evaluation metrics as in [36] [15] [19], including accuracy, average precision, average recall, and average F-1 score.”