Clothing Attribute Recognition Based on RCNN Framework Using L-Softmax Loss

Xiang, Jun; Dong, Tiantian; Pan, Ruru; Gao, Weidong

doi:10.1109/access.2020.2979164

Cited by 32 publications

(11 citation statements)

References 38 publications

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“…A similar study [39] used the R-CNN framework for clothing attribute recognition. The proposed regions of the clothes are extracted by applying the modified selective search algorithm.…”

Section: Proposed Approachmentioning

confidence: 99%

A Framework for Pedestrian Attribute Recognition Using Deep Learning

et al. 2022

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The pedestrian attribute recognition task is becoming more popular daily because of its significant role in surveillance scenarios. As the technological advances are significantly more than before, deep learning came to the surface of computer vision. Previous works applied deep learning in different ways to recognize pedestrian attributes. The results are satisfactory, but still, there is some scope for improvement. The transfer learning technique is becoming more popular for its extraordinary performance in reducing computation cost and scarcity of data in any task. This paper proposes a framework that can work in surveillance scenarios to recognize pedestrian attributes. The mask R-CNN object detector extracts the pedestrians. Additionally, we applied transfer learning techniques on different CNN architectures, i.e., Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2. The main contribution of this paper is fine-tuning the ResNet 152 v2 architecture, which is performed by freezing layers, last 4, 8, 12, 14, 20, none, and all. Moreover, data balancing techniques are applied, i.e., oversampling, to resolve the class imbalance problem of the dataset and analysis of the usefulness of this technique is discussed in this paper. Our proposed framework outperforms state-of-the-art methods, and it provides 93.41% mA and 89.24% mA on the RAP v2 and PARSE100K datasets, respectively.

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“…A similar study [39] used the R-CNN framework for clothing attribute recognition. The proposed regions of the clothes are extracted by applying the modified selective search algorithm.…”

Section: Proposed Approachmentioning

confidence: 99%

A Framework for Pedestrian Attribute Recognition Using Deep Learning

et al. 2022

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“…Recent works have shown growing interest in training machines for effective visual recognition on large-scale image datasets. Existing research on fashion attribute recognition covers the tasks of image parsing [13], [16], image classification [17]- [22], and classification with noisy labels [5], [23]. 1 https://wear.jp/.…”

Section: A Fashion Attribute Recognitionmentioning

confidence: 99%

“…The goal of image classification is to predict a single label or a set of labels for a given image. Image classification models often employ CNNs to extract features related to shapes and textures in an image and to generate predictions of relevant attributes [17]. Similar ideas have been extended to clothing recommendation [18] and fashion image retrieval [19], [20].…”

Section: A Fashion Attribute Recognitionmentioning

confidence: 99%

From Street Photos to Fashion Trends: Leveraging User-Provided Noisy Labels for Fashion Understanding

Huang

Hsu

2021

IEEE Access

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There is increased interest in using street photos to understand fashion trends. Though street photos usually contain rich clothing information, there are several technical challenges to their analysis. First, street photos collected from social media sites often contain user-provided noisy labels, and training models using these labels may deteriorate prediction performance. Second, most existing methods predict multiple clothing attributes individually and do not consider the potential to share knowledge between related tasks. In addition to these technical challenges, most fashion image datasets created by previous studies focus on American and European fashion styles. To address these technical challenges and understand fashion trends in Asia, we created RichWear, a new street fashion dataset containing 322,198 images with various text labels for fashion analysis. This dataset, collected from an Asian social network site, focuses on street styles in Japan and other Asian areas. RichWear provides a subset of expert-verified labels in addition to user-provided noisy labels for model training and evaluation. We propose the Fashion Attributes Recognition Network (FARNet) based on the multi-task learning framework to improve fashion recognition. Instead of predicting each clothing attribute individually, FARNet predicts three types of attributes simultaneously, and, once trained, this network leverages the noisy labels and generates corrected labels based on the input images. Experimental results show that this approach significantly outperforms existing methods. Applying the trained model to the RichWear dataset, we report Asian fashion trends and street styles based on predicted labels and image clusters from latent feature vectors.

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“…Liu et al presented us a DeepFashion network for clothing attribution recognition. Xiang et al (2020) proposed a clothing attribute recognition based RCNN framework by using L-Softmax loss. These three methods focus on the attribute recognition of a certain type of clothing, and the recognition accuracy of categories needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Clothing recognition based on deep sparse convolutional neural network

Xiang

Pan

Gao

2022

IJCST

Self Cite

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Purpose The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.Design/methodology/approach The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.Findings Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.Originality/value Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.

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Clothing Attribute Recognition Based on RCNN Framework Using L-Softmax Loss

Cited by 32 publications

References 38 publications

A Framework for Pedestrian Attribute Recognition Using Deep Learning

A Framework for Pedestrian Attribute Recognition Using Deep Learning

From Street Photos to Fashion Trends: Leveraging User-Provided Noisy Labels for Fashion Understanding

Clothing recognition based on deep sparse convolutional neural network

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