Real-time one-shot learning gesture recognition based on lightweight 3D Inception-ResNet with separable convolutions

Li, Lianwei; Qin, Shiyin; Lu, Zhi; Zhang, Dinghao; Xu, Kuanhong; Hu, Zhongying

doi:10.1007/s10044-021-00965-1

Cited by 6 publications

(1 citation statement)

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“… Jiang et al (2020) constructed a strongly and weakly supervised coupled network system for visual sentiment differentiation of images by importing images into VGGNet, obtaining the entire image features from the fifth convolutional layer, and then using a spatial pooling strategy to obtain weights for each emotion type. Li et al (2021b) proposed a 3D CNN combining the 3D Inception-ResNet layer and LSTM network to extract image spatial features using Inception ResNet and learn temporal relationships using LSTM, then apply this information for classification.…”

Section: Related Workmentioning

confidence: 99%

Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm

Yang,

Chen

2023

PeerJ Computer Science

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Image sentiment analysis technology can predict, measure and understand the emotional experience of human beings through images. Aiming at the problem of extracting emotional characteristics in art appreciation, this article puts forward an innovative method. Firstly, the PageRank algorithm is enhanced using tweet content similarity and time factors; secondly, the SE-ResNet network design is used to integrate Efficient Channel Attention (ECA) with the residual network structure, and ResNeXt50 is optimized to enhance the extraction of image sentiment features. Finally, the weight coefficients of overall emotions are dynamically adjusted to select a specific emotion incorporation strategy, resulting in effective bimodal fusion. The proposed model demonstrates exceptional performance in predicting sentiment labels, with maximum classification accuracy reaching 88.20%. The accuracy improvement of 21.34% compared to the traditional deep convolutional neural networks (DCNN) model attests to the effectiveness of this study. This research enriches images and texts’ emotion feature extraction capabilities and improves the accuracy of emotion fusion classification.

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Section: Related Workmentioning

confidence: 99%