EPYNET: Efficient Pyramidal Network for Clothing Segmentation

Inácio, Andrei de Souza; Lopes, Heitor Silvério

doi:10.1109/access.2020.3030859

Cited by 11 publications

(11 citation statements)

References 41 publications

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“…There are also many research works focusing on segmentation of clothing to enable ne image editing of clothing. Inacio et al [14] proposed a framework called EPYNET for extracting clothing features, which is based on SSDs and FPNs, with the E cientNet model as the backbone to improve the accuracy of segmentation. Zhang et al [15] proposed a new framework called ClothingOut, a new framework that utilizes GAN to solve the clothing transformation problem from images containing human bodies to at clothing images.…”

Section: Related Workmentioning

confidence: 99%

A Fine-grained Attributes Recognition Model for Clothing Based on Improved the CSPDarknet and PAFPN Network

Pan,

Xiang,

Zhang

et al. 2024

Preprint

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An efficient and accurate recognition model for fine-grained attributes of clothing has significant business prospects and social implications. However, the inherent diversity and complexity of clothing makes acquiring datasets with fine-grained attributes a costly endeavor. To address these challenges, we propose a lightweight clothing fine-grained attributes recognition model. First, the Ghost module is introduced into the CSPDarknet network to enhance the depth and expressiveness of feature learning while reducing the parameters and computational complexity. Then, the Conv module is replaced with the GSConv module in the PAFPN network to further reduce the network computational load, and the SE attention mechanism is also added to enhance the perception of key features. Finally, the Detect module is utilized to achieve effective recognition of fine-grained attributes of clothing. To evaluate the model performance, we construct a clothing dataset containing 20 fine-grained attributes. The experimental results show that the model achieves precision, recall and mAP of 76.2%, 78.9% and 81.7%. Compared to the original model, the parameters are reduced by 26.2%, and the FPS is improved by 25.4%. Our proposed model performs well on the small-scale dataset and improves its performance in resource-constrained environments, which has practical applications in clothing recommendation, virtual fitting, and personalization.

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Section: Related Workmentioning

confidence: 99%

A Fine-grained Attributes Recognition Model for Clothing Based on Improved the CSPDarknet and PAFPN Network

Pan,

Xiang,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…• UTFPR-SBD3 (2020): In [56], the authors constructed UTFPR-SBD3, intended for clothing segmentation in the context of soft biometrics. The dataset is composed of 4 500 images manually annotated into 18 classes and an addition class for the background.…”

Section: Tablementioning

confidence: 99%

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

et al. 2022

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Fashion knowledge encourages people to properly dress and faces not only physiological necessity of users, but also the requirement of social practices and activities. It usually includes three jointly related aspects of: occasion, person and clothing. Nowadays, social media platforms allow users to interact with each other online to share opinions and information. The use of social media sites such as Instagram has already spread to almost every fashion brand and been evaluated as business take-off tools. With the heightened use of social media as a means of marketing communication for fashion brands, it has become necessary to empirically analyse and extract fashion knowledge from them. Thus, social brands are investing on them. In this way, they can understand the consumer's preferences. This change is also having a significant impact on social media data analysis. To solve this issue, the Deep learning (DL) methods are proven to be effective solutions due to their automatic learning capability. However, little systematic work currently exists on how researchers have applied DL for analysing fashion knowledge from social media data. Hence, this contribution outlines DL-based techniques for social media data related to fashion domain. In this study, a review of the dataset within the fashion world and the DL methods applied on, it is presented to help out new researchers interested in this subject. In particular, five different tasks will be considered: Object Detection, that includes Clothes Landmark Detection, Clothes Parsing and Product Retrieval, Fashion Classification, Clothes Generation, Automatic Fashion Knowledge Extraction and Clothes Recommendation. Therefore, the purpose of this paper is to underline the multiple applications within the fashion world using deep learning techniques. However, this review does not cover all the methods used: in fact, only Deep Learning methods have been analyzed. This choice was made since, given the huge amount of fashion social media data that has been collected, Deep Learning methods achieve the best performance both in terms of accuracy and time. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.

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“…Therefore, the segmentation models cannot reliably forecast classifications due to the presence of confusing semantic information. In addition, the visual variations of targets, such as resolution, deformities, occlusions and background, impose great challenges for segmentation [5,6]. Early work on the fashion parsing problem usually adopted the modified image segmentation models, e.g., Grab Cut and the Markov Random Field (MRF).…”

Section: Introductionmentioning

confidence: 99%

P D N: A Priori Dictionary Network for Fashion Parsing

Hou,

Lu,

Yang

et al. 2024

Applied Sciences

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The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on improving datasets and supplying abundant priori information, but the utilization of features by more efficient methods is rarely explored. In this paper, we propose a multi-scale fashion parsing model called the Priori Dictionary Network (PDN), which includes a priori attention module and a multi-scale backbone. The priori attention module extracts high dimensional features from our designed clothing average template as a priori information dictionary (priori dictionary, PD), and the PD is utilized to activate the feature maps of a CNN from a multi-scale attention mechanism. The backbone is derived from classical models, and five side paths are designed to leverage the richer features of local and global contextual representations. To measure the performance of our method, we evaluated the model on four public datasets, the CFPD, UTFR-SBD3, ModaNet and LIP, and the experimental results show that our model stands out from other State of the Art in all four datasets. This method can assist with the labeling problem of clothing datasets.

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EPYNET: Efficient Pyramidal Network for Clothing Segmentation

Cited by 11 publications

References 41 publications

A Fine-grained Attributes Recognition Model for Clothing Based on Improved the CSPDarknet and PAFPN Network

A Fine-grained Attributes Recognition Model for Clothing Based on Improved the CSPDarknet and PAFPN Network

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

P D N: A Priori Dictionary Network for Fashion Parsing

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