Iterative Visual Relationship Detection via Commonsense Knowledge Graph

Wan, Hai; Liang, Jinrui; Du, Jianfeng; Liu, Yanan; Ou, Jialing; Wang, Baoyi; Pan, Jeff Z.; Zeng, Juan

doi:10.1016/j.bdr.2020.100175

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Scholars have proposed approaches to identify generic users and usage context from consumer opinions that could stimulate ideas for storyboarding. Object-detection algorithms coupled with knowledge graphs (Wan et al 2021) could be useful for labelling and describing scenes like storyboards. Kansei engineering methods could be adopted to capture emotional feedback on the storyboards.…”

Section: Discussionmentioning

confidence: 99%

Natural language processing in-and-for design research

2022

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We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

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Section: Discussionmentioning

confidence: 99%

Natural language processing in-and-for design research

2022

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Knowledge Enhanced Zero-Shot Visual Relationship Detection

Ding,

Lai,

Liu

2024

Knowledge Science, Engineering and Management

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Computer Vision-Based Hazard Identification of Construction Site Using Visual Relationship Detection and Ontology

Han

et al. 2022

Buildings

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Onsite systematic monitoring benefits hazard prevention immensely. Hazard identification is usually limited due to the semantic gap. Previous studies that integrate computer vision and ontology can address the semantic gap and detect the onsite hazards. However, extracting and encoding regulatory documents in a computer-processable format often requires manual work which is costly and time-consuming. A novel and universally applicable framework is proposed that integrates computer vision, ontology, and natural language processing to improve systematic safety management, capable of hazard prevention and elimination. Visual relationship detection based on computer vision is used to detect and predict multiple interactions between objects in images, whose relationships are then coded in a three-tuple format because it has abundant expressiveness and is computer-accessible. Subsequently, the concepts of construction safety ontology are presented to address the semantic gap. The results are subsequently recorded into the SWI Prolog, a commonly used tool to run Prolog (programming of logic), as facts and compared with triplet rules extracted from using natural language processing to indicate the potential risks in the ongoing work. The high-performance results of Recall@100 demonstrated that the chosen method can precisely predict the interactions between objects and help to improve onsite hazard identification.

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Iterative Visual Relationship Detection via Commonsense Knowledge Graph

Cited by 3 publications

References 5 publications

Natural language processing in-and-for design research

Natural language processing in-and-for design research

Knowledge Enhanced Zero-Shot Visual Relationship Detection

Computer Vision-Based Hazard Identification of Construction Site Using Visual Relationship Detection and Ontology

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