Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention

Li, Xin; Guo, Yuxin; Pan, Weiguo; Liu, Hongzhe; Xu, Bingxin

doi:10.3390/app13063614

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…To derive a set of feature vectors, the GhostNet model is used. The GhostNet model removes features with some parameters and efficiently receives unwanted data from the network [ 21 ]. The GhostNet element turns the typical convolutional function into 2‐step operations.…”

Section: The Proposed Modelmentioning

confidence: 99%

CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm

Gandikota,

S.,

2023

PLoS ONE

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Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC classification in computed tomography (CT) scans is a vital task that aims to accurately discriminate between tumorous and non-tumorous pancreatic tissues. CT images provide detailed cross-sectional images of the pancreas, which allows oncologists and radiologists to analyse the characteristics and morphology of the tissue. Machine learning (ML) approaches, together with deep learning (DL) algorithms, are commonly explored to improve and automate the performance of PC classification in CT scans. DL algorithms, particularly convolutional neural networks (CNNs), are broadly utilized for medical image analysis tasks, involving segmentation and classification. This study explores the design of a tunicate swarm algorithm with deep learning-based pancreatic cancer segmentation and classification (TSADL-PCSC) technique on CT scans. The purpose of the TSADL-PCSC technique is to design an effectual and accurate model to improve the diagnostic performance of PC. To accomplish this, the TSADL-PCSC technique employs a W-Net segmentation approach to define the affected region on the CT scans. In addition, the TSADL-PCSC technique utilizes the GhostNet feature extractor to create a group of feature vectors. For PC classification, the deep echo state network (DESN) model is applied in this study. Finally, the hyperparameter tuning of the DESN approach occurs utilizing the TSA which assists in attaining improved classification performance. The experimental outcome of the TSADL-PCSC method was tested on a benchmark CT scan database. The obtained outcomes highlighted the significance of the TSADL-PCSC technique over other approaches to PC classification.

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Section: The Proposed Modelmentioning

confidence: 99%

CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm

Gandikota,

S.,

2023

PLoS ONE

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“…The attention mechanism has been increasingly applied in the field of image generation to enhance the extraction of specific features. For example, channel attention and spatial attention make the model focus on informative features [18]. The transformer developed recently in vision can effectively capture global attention due to self-attention [19].…”

Section: Introductionmentioning

confidence: 99%

A de-texturing model for enhancing the accuracy of centroid positioning

Wang,

Xu,

Wang

et al. 2024

Meas. Sci. Technol.

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In tasks guided by microvision, extracting edges and centroids is a common method for positioning, which is negatively affected by texture. Here, an attention-related de-texturing model is proposed to eliminate the texture of microparts and preserve accurate edges. A network with an attention module called De-texturing Net is built, in which both the transformer and channel attention modules are included. Considering the importance of texture, the additional factor in loss function is constructed based on the Gram matrix difference between target images and generated images. Results show that De-texturing Net can generate de-texturized images with high PSNR/SSIM, indicating the similarity between de-texturized and target images. Moreover, for the centroid positioning, the error in de-texturized images is significantly lower than the error in original images. This study helps improve the accuracy of centroid positioning due to the de-texturized images with accurate edges.

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“…However, the operating window size in maximum and average pooling affects the ability of SA in preserving important features. To further enhance the convolution feature capabilities in SA and CA, a multi scale feature fusion attention was proposed with coordinate attention (CA) mechanism [8] on a light weight bidirectional feature pyramid network.…”

Section: Introductionmentioning

confidence: 99%

Machine Interpretation of Ballet Dance: Alternating Wavelet Spatial and Channel Attention Based Learning Model

Kishore,

Kumar,

Kumar

et al. 2024

IEEE Access

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Ballet' is a 15 th -century concert performing dance form that originated in Italy. Current AI models for ballet dance pose identification in live performance videos is challenging due to variational pixel distribution of human actions across backgrounds. Notably, their performance on online video datasets improved with both channel (CA) and spatial attention (SA) models but tend to generate over-smoothed Convolutional features due to feature averaging in the attention network. Alternatively, wavelet attention preserves both high and low frequency components in the features which improves the test accuracy. Applying CA and SA on wavelet features simultaneously resulted in hyper-refined features due to double averaging. To overcome this drawback, Alternating Wavelet Channel and Spatial Attention (AWCSA) across any learning network as backbone architecture is proposed. The global features across the residual connections in the backbone (ResNet50) are amplified exclusively with low and high-frequency local features across the channel and spatial dimensions alternatively one after the other. The Ballet online dance video dataset (BOVD23) evaluates the performance of the proposed AWCSA along with baseline action datasets. The end-to-end trained AWCSA has recorded a 6-8% higher performance metrics on BOVD23 dataset over the counterparts.

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Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention

Cited by 6 publications

References 43 publications

CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm

CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm

A de-texturing model for enhancing the accuracy of centroid positioning

Machine Interpretation of Ballet Dance: Alternating Wavelet Spatial and Channel Attention Based Learning Model

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