Conditional progressive network for clothing parsing

Su, Zhuo; Zhang, Gengwei; Luo, Xianghui; Wang, Ruomei; Zhou, Fan

doi:10.1049/iet-ipr.2018.5494

Cited by 8 publications

(8 citation statements)

References 32 publications

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“…is actually reflects the most aesthetic features of freehand brushwork. Stage costumes can also express a series of psychological changes such as anger, anxiety, and joy of the opera characters and have good dramatic effects in expressing the feelings of the characters and portraying the personalities of the characters in the drama [2]. erefore, it is of vital importance to analyze the artistic characteristics of stage costumes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Luo

2021

Security and Communication Networks

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In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data. Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data. On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function. Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed. Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities.

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Section: Introductionmentioning

confidence: 99%

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Luo

2021

Security and Communication Networks

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“…Feature resolution, global context information and edge details were used to design a context embedding with edge perceiving (CE2P) framework [21] for human parsing. Su et al [47] used a pose estimation network module to provide pose heatmaps about the human pose information for human parsing. Zhang et al [48] proposed a Correlation Parsing Machine to take advantage of both edge and pose features to improve human parsing.…”

Section: Human Parsingmentioning

confidence: 99%

Enhancing human parsing with region‐level learning

Zhou

Mok

2023

IET Computer Vision

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Human parsing is very important in a diverse range of industrial applications. Despite the considerable progress that has been achieved, the performance of existing methods is still less than satisfactory, since these methods learn the shared features of various parsing labels at the image level. This limits the representativeness of the learnt features, especially when the distribution of parsing labels is imbalanced or the scale of different labels is substantially different. To address this limitation, a Region‐level Parsing Refiner (RPR) is proposed to enhance parsing performance by the introduction of region‐level parsing learning. Region‐level parsing focuses specifically on small regions of the body, for example, the head. The proposed RPR is an adaptive module that can be integrated with different existing human parsing models to improve their performance. Extensive experiments are conducted on two benchmark datasets, and the results demonstrated the effectiveness of our RPR model in terms of improving the overall parsing performance as well as parsing rare labels. This method was successfully applied to a commercial application for the extraction of human body measurements and has been used in various online shopping platforms for clothing size recommendations. The code and dataset are released at this link https://github.com/applezhouyp/PRP.

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“…By controlling the angles ∠(w r ∇v r , ∇v m D ) and ∠(w f ∇v f , ∇v m D ) to be acute (see Figure 2) when one of them becomes obtuse, one can prevent the issue that training may benefit one loss but significantly harm the other. However, in previous models [4][5][6][7][8][9][30][31][32][33][34], the loss function of the discriminator consists of two equally weighted parts, that is,…”

Section: Robustness Analysismentioning

confidence: 99%

“…Since the introduction of GANs, many variants have been proposed [2][3][4][5] to improve the generated image quality. Although GANs have already been successfully used in image generation and image editing [6][7][8][9], they are notoriously difficult to train, and it has been observed that they often suffer from mode collapse [10,11], which causes the generator network to produce results with poor generalizability; that is, a limited variety of samples are produced, but many other modes are missed.…”

Section: Introductionmentioning

confidence: 99%

Taming mode collapse in generative adversarial networks using cooperative realness discriminators

Chen

Zhu

et al. 2022

IET Image Processing

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Generative adversarial networks (GANs) are able to produce realistic images. However, GANs may suffer mode collapse in their output data distribution. Here, we theoretically and empirically justify generalizing the GAN framework to multiple discriminators with one generator for improving generative performance. First, a comprehensive perspective is adopted to understand why mode collapse occurs. Second, an array of cooperative realness discriminators is introduced into the GAN framework to combat mode collapse and explore discriminator roles ranging from a formidable adversary to a forgiving teacher. Third, two types of simple yet effective regularization are proposed for generating realistic and diverse images. Experiments on various datasets show the effectiveness of the GAN compared to previous methods in alleviating mode collapse and improving the quality of the generated samples.

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Conditional progressive network for clothing parsing

Cited by 8 publications

References 32 publications

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Enhancing human parsing with region‐level learning

Taming mode collapse in generative adversarial networks using cooperative realness discriminators

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