Parsing clothing in fashion photographs

By Kota Yamaguchi, M. Kiapour, Luis E. Ortiz, Tamara L. Berg. 2012

521 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Hypercolumns for object segmentation and fine-grained localization (2014, methodology): “[41, 40] parse fashion photographs into clothing items.”
COCO-Stuff: Thing and Stuff Classes in Context (2016, methodology): “Several works use interactive segmentation methods [42, 57, 10] to speedup annotation; others annotate superpixels [61, 22, 40].”
DensePose: Dense Human Pose Estimation in the Wild (2018, background): “Human part segmentation masks have been provided in a number of datasets [48, 6, 13]; these can be understood as providing a coarsened version of image-to-surface correspondence, where rather than continuous coordinates one predicts discretized part labels [35].”
Deep Interactive Object Selection (2016, background): “In Figure 7 we show some segmentation results of clothing parts on the Fashionista dataset [27].”
Large scale visual recommendations from street fashion images (2014, background): “Other related papers on fashion domain [4, 9, 13, 14, 15] work on the problem of fashion parsing and similarity retrieval where the goal is to retrieve similar fashion image for a given query.”
Instance-level Human Parsing via Part Grouping Network (2018, background): “Human Parsing Recently, many research eﬀorts have been devoted to human parsing [23,42,41,35,26,39,5,11] for advancing human-centric analysis research.”
Joint Object and Part Segmentation Using Deep Learned Potentials (2015, background): “However, due to its increased challenge in getting detailed boundaries, most previous work focused on parsing objects given both the semantic category and a cropped bounding box with no occlusion, such as human parsing [4, 45, 41, 13, 11], car parsing [35, 13, 28] or animal parsing [39].”
Towards Unified Human Parsing and Pose Estimation (2014, methodology): “For the representation of semantic regions, we adopt the recently proposed Parselets [5].”
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation (2018, background): “WoW, Exact Street2Shop, and Fashion-136K are collected from street photos(5) and thus inevitably involve a clothing parsing technique, which still remains a great challenge in the computer vision domain [4], [46], [47].”
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net (2015, methodology): “Hence parsing humans in PASCAL is considerably more diﬃcult than in datasets, such as Fashionista [34], that were constructed solely to evaluate human parsing.”
Runway to Realway: Visual Analysis of Fashion (2015, methodology): “More closely related to the parsing approach used in this paper, there has been work on predicting semantic segmentations of clothing images [24, 25, 26], and we use the open source implementation from [26] as part of our pipeline.”
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images (2014, methodology): “The data set is more challenging than the Fashionista data set [64] that contains only 685 images.”
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.”
Semantic part segmentation using compositional model combining shape and appearance (2014, background): “But there are only a few pieces of works on semantic part segmentation, such as human parsing [4, 10, 9, 29] and car parsing [26, 11, 23].”
Garment modeling with a depth camera (2015, unknown): “Recently, some garment image parsing methods [Yamaguchi et al. 2012; Liu et al. 2012; Yamaguchi et al. 2015; Yang et al. 2014] are proposed in the field of computer vision.”
Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network (2015, background): “Clothing parsing methods, which produce semantic labels for each pixel in the input image, have received significant attention in the past few years [45, 9].”
Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos (2013, methodology): “A recent work closely related to ours is the work by Yamaguchi et al. [16].”
Superpixels: An evaluation of the state-of-the-art (2016, methodology): “We found that UE tends to correlate strongly with ASA which can be explained by Equations (3) and (6), respectively.”
Human Pose Estimation Using Body Parts Dependent Joint Regressors (2013, methodology): “We did not annotate the head by the top of the head and the neck as in other datasets [39, 18] since these two points were very difficult to annotate accurately.”