Predicting occupation via human clothing and contexts

Zheng, Shuguo; Wang, Meng; Hua, Xian-Sheng; Yan, Shuicheng

doi:10.1109/iccv.2011.6126355

Cited by 95 publications

(77 citation statements)

References 13 publications

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“…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. There is also related work on recognizing attributes or concepts that are strongly related to clothing, for example, occupation [22,21] or social identity [15]. In this paper, we take a closer look at learning human based judgments of outfit similarity and also look at detecting visual trends in fashion over time.…”

Section: Related Workmentioning

confidence: 99%

Runway to Realway: Visual Analysis of Fashion

Vittayakorn

Yamaguchi

Berg

et al. 2015

2015 IEEE Winter Conference on Applications of Computer Vision

102

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Section: Related Workmentioning

confidence: 99%

Runway to Realway: Visual Analysis of Fashion

Vittayakorn

Yamaguchi

Berg

et al. 2015

2015 IEEE Winter Conference on Applications of Computer Vision

102

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“…are also considered to prove the outfit recommendation system performance good. As for color, O'Donovan P proposes a model that can rate the preference of people to 5 color combinations [18], which can be used in outfit to see if the color of the outfit is proved to be a good combination or not; And the same with style, which is used in [19] to recommend items according style. These attributes of items can be used as a method to prove that the judgement or the recommendation of our outfit is good or not.…”

Section: Related Workmentioning

confidence: 99%

Outfit Recommendation System Based on Deep Learning

Huang¹,

Huang²

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. In this paper, we propose an outfit recommendation system based on deep learning. Our goal is to use the system not only to judge an outfit if it is good or not but also to recommend good outfit to users when it is given a pool of cloth items. Our proposed model includes two parts: one is feature extractor based on ResNet-50, and the other is a binary classifier which is to classify the outfits into good ones and bad ones. Since our model is based on deep learning, it is necessary to use huge data to train the model. We collected a dataset which consists of 409,776 outfits with 644,192 items from the famous fashion website called Polyvore.com. With this dataset, we trained our model and the performance of it is over 84%. And our model can also recommend daily outfit to users.

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“…Common examples are clothing classification or retrieval [3,2,23], clothing parsing (i.e., semantically label each image pixel) [29,21,4,15] or higher level tasks such as evaluating style or deducting people's occupation or social tribe [24,11,28,22,17].…”

Section: State Of the Artmentioning

confidence: 99%