A scale space model of weighted average CNN ensemble for ASL fingerspelling recognition

Aloysius, Neena; Geetha, M. Kalaiselvi

doi:10.1504/ijcse.2020.10029229

Cited by 2 publications

(2 citation statements)

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“…Among the classical neural network models, recurrent neural networks (RNN) [20] and convolutional neural networks (CNN) [21] are widely used for sentiment classification tasks. The powerful text feature extraction ability of RNN and CNN improves the accuracy of sentiment classification [22][23].…”

Section: Classical Neural Networkmentioning

confidence: 99%

TS-GCN: Aspect-level sentiment classification model for consumer reviews

Zhang

Zhao

et al. 2023

ComSIS

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The goal of aspect-level sentiment classification (ASC) task is to obtain the sentiment polarity of aspect words in the text. Most existing methods ignore the implicit aspects, resulting in low classification accuracy. To improve the accuracy, this paper proposes a classification model for consumer reviews, abbreviated as TS-GCN (Truncated history attention and Selective transformation network-Graph Convolutional Networks). TS-GCN can classify sentiment from both explicit and implicit aspects. Firstly, we process the text by the BERT model and the BiLSTM model to obtain the text features. Secondly, the GCN model completes explicit sentiment classification by training text features. Due to the lack of implicit words, the GCN model cannot classify implicit sentiments. Finally, we predict implicit words based on the TS model, which makes up for the deficiency of the GCN model and completes the sentiment classification of implicit words. TS-GCN is proved on several datasets in the consumer reviews field. The results of experiments show that the TS-GCN can improve the accuracy and F1 of ASC.

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Section: Classical Neural Networkmentioning

confidence: 99%

TS-GCN: Aspect-level sentiment classification model for consumer reviews

Zhang

Zhao

et al. 2023

ComSIS

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“…In recent years, deep convolutional neural networks-based object detection methods have been widely studied and applied in many fields such as human face detection (Gu et al, 2020), gesture recognition (Aloysius and Geetha, 2020), medical image analysis (Zhou et al, 2019), etc. Yet most of them require lots of labelled data to guarantee the detection accuracy and avoid the overfitting.…”

Section: Introductionmentioning

confidence: 99%

Prior distributions-based data augmentation for object detection

Sun¹,

Luo²,

Ma³

et al. 2022

IJCSE

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To solve the problem of data hungry, data augmentation methods based on cut-and-paste that can explore the visual context are widely used. However, these methods either limit the expansion of the instances diversity of dataset or increase the computational burden. In this paper, we propose a novel data augmentation strategy based on prior distributions, which can be used to guide data augmentation for object detection. On one hand, the method can effectively capture the relationship between the foreground instance and the visual context. On the other hand, it can increase the instances diversity of the original dataset as much as possible. Experimental results show that the performance of the popular object detection model can be effectively improved by expanding the original dataset with our method. Compared with the baseline, our method improves by 0.8 percentage point on PASCAL VOC and 1.1 percentage points higher on cross-data test set.

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A scale space model of weighted average CNN ensemble for ASL fingerspelling recognition

Cited by 2 publications

References 0 publications

TS-GCN: Aspect-level sentiment classification model for consumer reviews

TS-GCN: Aspect-level sentiment classification model for consumer reviews

Prior distributions-based data augmentation for object detection

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