Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism

Zhang, Han; Bai, Luyi

doi:10.1016/j.neunet.2023.01.043

Cited by 20 publications

(2 citation statements)

References 39 publications

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“…Attention mechanisms have significantly transformed the field of computer vision, particularly in tasks such as action recognition, tracking, and various others, by improving the understanding of relational dynamics 36,37 . In the specific area of human-object interaction (HOI) detection, where modeling intricate relationships is essential, attention mechanisms have proven to be exceptionally beneficial [20][21][22][23][24] .…”

Section: Attention Mechanismmentioning

confidence: 99%

Enhanced Human-Object Interaction Detection via Maximum IoU Partitioning and Chunk Block Attention

Lee,

Kang,

Park

et al. 2024

Preprint

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Accurate understanding of spatial relationships between humans and objects is a key for recognizing the Human-Object Interaction (HOI).Due to its inherent simplicity and interpretability, Intersection over Union (IoU) has established itself as the predominant metric for expressing spatial relationships.This paper presents a simple yet effective attention mechanism that leverages the IoU metric.Specifically, we introduce a Chunk Block Attention (CBA) module that computes spatial-aware attention weights of image slices extracted through the IoU-guided Human-Object association method.The CBA uniquely integrates self-attention and cross-attention to capture internal and external relationships within slices.Through extensive experiments on various HOI models, we validate the effectiveness of the proposed method.Experimental results show consistent improvements in qualitative measure and impressive interpretability by visualizing the learned attention weights.Furthermore, we provide detailed analysis and visualizations of the attention weights learned by the CBA module.This reveals insights into how the model focuses on relevant spatial and semantic relationships for recognizing complex human-object interactions.

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Section: Attention Mechanismmentioning

confidence: 99%

Enhanced Human-Object Interaction Detection via Maximum IoU Partitioning and Chunk Block Attention

Lee,

Kang,

Park

et al. 2024

Preprint

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“…AM is a mechanism that can automatically recognize and focus on important features. This study introduces AM into the facial action network architecture to capture and process facial action related information [ 19 ]. Fig 2 shows the AM network structure that integrates channels and space.…”

Section: Computer Frt For Digital Librariesmentioning

confidence: 99%

Facial length and angle feature recognition for digital libraries

Li,

Ji,

Chen

et al. 2024

PLoS ONE

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With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based on length and angle features of facial expressions, the expression recognition network is explored to improve the recognition rate of different expressions. Finally, an end-to-end network framework based on attention mechanism for facial feature points is constructed to improve the accuracy and stability of facial feature recognition network. To verify the effectiveness of the proposed method, experiments were conducted using the facial expression dataset FER-2013. The experimental results showed that the average recognition rate for the seven common expressions was 97.28% to 99.97%. The highest recognition rate for happiness and surprise was 99.97%, while the relatively low recognition rate for anger, fear, and neutrality was 97.18%. The data has verified that the research method can effectively recognize and distinguish different facial expressions, with high accuracy and robustness. The recognition method based on attention mechanism for facial feature points has effectively optimized the recognition process of facial length and angle features, significantly improving the stability of facial expression recognition, especially in complex environments, providing reliable technical support for digital libraries and other fields. This study aims to promote the development of facial recognition technology in digital libraries, improve the service quality and user experience of digital libraries.

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A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

Liu,

2024

Engineering Reports

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Maintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowledge extraction. Addressing this, this research develops effective methods to extract task‐centric maintenance knowledge from unstructured PDF manuals. A new Task‐centric Knowledge Graph (TCKG) schema centralized on maintenance task components (MTCs) is proposed to address the need for structured knowledge representation. A method (Heterogeneous Graph‐based Method, HGM) for knowledge extraction is then proposed, which is enhanced by incorporating visual and spatial information. In the experiments, the proposed HGM exhibits robust performance in the knowledge extraction process, surpassing the baseline Graph‐based Interaction Model with a Tracker (GIT) method in MTCs extraction by 13.3%, and the baseline Translate Embedding (TransE) method in MTCs' relation extraction by 3.8%. A series of ablation studies also prove that including visual and spatial information through the proposed method can improve the relation extraction performance by over 10%. This research supplies valuable insights for future developments in information extraction from maintenance manuals.

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Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism

Cited by 20 publications

References 39 publications

Enhanced Human-Object Interaction Detection via Maximum IoU Partitioning and Chunk Block Attention

Enhanced Human-Object Interaction Detection via Maximum IoU Partitioning and Chunk Block Attention

Facial length and angle feature recognition for digital libraries

A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

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