Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Samkari, Esraa; Arif, Muhammad; Alghamdi, Manal; Al Ghamdi, Mohammed A.

doi:10.3390/make5040081

Cited by 19 publications

(4 citation statements)

References 159 publications

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“…It is worth noting that in addition to image segmentation tasks, U-Net is also used as a feature extractor in various applications. Samkari et al [49] also used U-Net in the field of human pose estimation.…”

Section: U-net and Skip Connectionmentioning

confidence: 99%

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

Wang,

Zhang,

Tao

et al. 2024

CMC

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While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity and texture information for enhanced fusion. Local contrast enhancement module (CEM) and texture detail enhancement module (DEM) are devised to process the decomposed images, followed by image fusion through the decoder. The proposed loss function ensures effective retention of key information from the source images of both modalities. Extensive comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture details. From the qualitative results, we can see the advantages of fusion details and local contrast. In the quantitative experiments, entropy (EN), mutual information (MI), structural similarity (SSIM) and other results have improved and exceeded the SOTA (State of the Art) model as a whole.

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Section: U-net and Skip Connectionmentioning

confidence: 99%

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

Wang,

Zhang,

Tao

et al. 2024

CMC

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“…Recently, the utilization of ANNs within various domains has experienced remarkable growth (e.g., [37][38][39][40][41][42][43][44]). In an educational context, this surge in applications includes diverse functionalities, such as predicting student performance, as demonstrated by the works of [9,10,[45][46][47].…”

Section: Neural Network In Educationmentioning

confidence: 99%

Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education

Hooshyar,

Azevedo,

Yang

2024

MAKE

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Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating symbolic educational knowledge (e.g., causal relationships and practitioners’ knowledge) in their development, (ii) a propensity to learn and reflect biases, and (iii) a lack of interpretability. As education is classified as a ‘high-risk’ domain under recent regulatory frameworks like the EU AI Act—highlighting its influence on individual futures and discrimination risks—integrating educational insights into ANNs is essential. This ensures that AI applications adhere to essential educational restrictions and provide interpretable predictions. This research introduces NSAI, a neural-symbolic AI approach that integrates neural networks with knowledge representation and symbolic reasoning. It injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking, aiming to enhance personalized learning and develop computational thinking skills. Our findings revealed that the NSAI approach demonstrates better generalizability compared to deep neural networks trained on both original training data and data enriched by SMOTE and autoencoder methods. More importantly, we found that, unlike traditional deep neural networks, which mainly relied on spurious correlations in their predictions, the NSAI approach prioritizes the development of robust representations that accurately capture causal relationships between inputs and outputs. This focus significantly reduces the reinforcement of biases and prevents misleading correlations in the models. Furthermore, our research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications.

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“…CNNs have achieved tremendous success in the field of human pose estimation [51]. Hourglass [36] belongs to the hourglass type of network structure, which can perceive more global information.…”

Section: Human Pose Estimationmentioning

confidence: 99%

A Lightweight Context-Aware Feature Transformer Network for Human Pose Estimation

Ma,

Shi,

Zhang

2024

Electronics

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We propose a Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design the Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes 3×3 convolution in the bottleneck of HRNet with a Contextual Transformer (CoT) block while reducing the complexity of the network. Specifically, the CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on query and contextualization keys, dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module (AFAM). Technically, given an intermediate input, the AFAM successively deduces attention maps along the channel and spatial dimensions. Then, an adaptive refinement module (ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above procedures, our lightweight network provides powerful clues for the detection of keypoints. Experiments are performed on the COCO and MPII datasets. The model achieves a 76.2 AP on the COCO val2017 dataset. Compared to other methods with a CNN as the backbone, CaFTNet has a 72.9% reduced number of parameters. On the MPII dataset, our method uses only 60.7% of the number of parameters, acquiring similar results to other methods with a CNN as the backbone.

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Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Cited by 19 publications

References 159 publications

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education

A Lightweight Context-Aware Feature Transformer Network for Human Pose Estimation

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