Landmark‐free clothes recognition with a two‐branch feature selective network

Lee, Sumin; Eun, Hyunjun; Oh, Sungchan; Kim, W; Jung, Chanho; Kim, Changick

doi:10.1049/el.2019.0660

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…erefore, the current English evaluation model often adopts the fixed input characteristics, proposes a cluster analysis method of specific data information, and collects audio input information in real time. In order to improve the efficiency of English text evaluation, scholars put forward an innovative evaluation method based on neural network algorithm and relevant theories [9]. In addition, according to the analysis of English characteristics, they proposed that attention should be paid to the development and construction of English text analysis system based on the limiting factors, and the management and importance of data information in the process of English feature scoring was realized.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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“…Lee et al used stack self-coding network for speech feature coding and compressed the data to a preset length with minimum reconstruction error [10]. Bibi et al have explored various DL (deep learning) frameworks in speech emotion tasks, and their experiments have demonstrated that feedforward and RNN (recurrent neural network) structures and their variants can be used to assist speech recognition, especially emotion recognition [11].…”

Section: Mnfr-related Research Nazemi Et Al Comprehensivelymentioning

confidence: 99%

Musical Note Feature Recognition Based on Long Short-Term Memory

Yuan

2022

Mobile Information Systems

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The timbre and volume of a single tone are among its fundamental characteristics. The single-tone detection technology is the key to the foundation of MNFR (musical note feature recognition), which is built on the fundamental feature extraction of single tones. A MNFR method based on LSTM (long short-term memory) is proposed because traditional methods have low accuracy in note feature classification and low accuracy in MNFR. To process the series of convolution feature maps, the feature maps are directly input into LSTM to learn hash codes. Extract the note features. Segment according to the changing trend of the physical features of the notes. Additionally, a number of feature maps are built from convolution feature maps extracted from numerous convolution layers of previously trained CNNs (convolutional neural networks), taking into account the spatial specifics and semantic features. The note start vector is produced using the enhanced peak extraction algorithm based on Gaussian kernel smoothing. The findings indicate that, when 100 samples are used, this method’s note classification accuracy differs from that of the DBN (deep belief network) and DWT (discrete wavelet transform) by 1.17 percent and 2.04 percent, respectively. The analysis in the conclusion demonstrates that the algorithm put forth in this paper is both theoretically and practically workable.

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“…Lee et al [8] proposed a landmark-free clothes classification via exploiting feature selective network based on VGG-16. A multitask learning network is divided into attribute prediction and category classification.…”

Section: Landmark-free Approachesmentioning

confidence: 99%

“…For category classification and attribute prediction, we applied top-k classification accuracy and top-k recall rate, respectively. We compared the performance with ten recently reported works [4,5,7,8,14,27,30,31,42,43] in fashion analysis. As shown in the Table 2, our model slightly outperforms state-of-the-art approaches using supervised learning in fashion clothing classification.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…As seen, earlier fashion models relied primarily on handcrafted functions and search for powerful clothing displays such as graphic models, context information, general object suggestions, human parts, boundingboxes and semantic masks. In these days, many performances in fashion clothing analysis tasks have been repeatedly revealed that deep neural networks can achieve better performance with learning problems using a large-scale labelled data [3,4,5,6,7,8,9]. Inspired by visual attention mechanisms, many researchers have tried to model a soft attentive network to prompt the performance of computer vision tasks [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

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Multi-staged Feature-Attentive Network for Fashion Clothing Classification and Attribute Prediction

Shajini

Ramanan

2022

ELCVIA

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In the visual fashion clothing analysis, many researchers are attracted with the success of deep learning concepts. In this work, we introduce a multi-staged feature-attentive network to attain clothing category classification and attribute prediction. The proposed network in this work brings out a landmark-independent structure, whereas the existing landmark-dependent structures take up a lot of manpower for landmark annotation and also suffers from inter- and intra-individual variability. Our focus on this work is intensifying feature extraction by incorporating low-level and high-level feature fusion within fashion network. We are aiming on multi-level contextual features which utilise spatial and channel-wise information to create contextual feature supervision. Further, we enclose a semi-supervised learning approach to escalate fashion clothes analysis that utilises knowledge sharing among labelled and unlabelled data. To the best of our knowledge, this is the first attempt to investigate the semi-supervised learning in fashion clothing analysis by adopting multitask architecture which simultaneously study the clothing categories as well as its attributes. We evaluated the proposed approach on large-scale DeepFashion-C dataset while unlabelled dataset obtained from six publicly available fashion datasets. Experimental results show that the proposed architectures for supervised and semi-supervised learning entailing deep convolutional neural network outperforms many state-of-the-art techniques considerably, in fashion clothing analysis.

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Landmark‐free clothes recognition with a two‐branch feature selective network

Cited by 7 publications

References 9 publications

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Musical Note Feature Recognition Based on Long Short-Term Memory

Multi-staged Feature-Attentive Network for Fashion Clothing Classification and Attribute Prediction

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