Designing efficient convolutional neural network structure: A survey

Mi, Jian-Xun; Feng, Jie; Huang, Ke-Yang

doi:10.1016/j.neucom.2021.08.158

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Semantic Reasoning Engine1

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Cited by 13 publications

(2 citation statements)

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“…In the phase 3, the reuse of existing ontological resources (e.g. FOAF 3 or OWL Time 4 ) is also clarified.…”

Section: Semantic Reasoning Enginementioning

confidence: 99%

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D-STOR: A Novel Framework of Deep-Semantic Traffic Object Recognition

Nguyen

2023

HueUni-JTT

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Deep learning techniques such as Convolutional Neural Networks (CNNs) have proven the efficiency in recognizing image objects. Moreover, this recognition work has been extended to discover relations among detected objects. Although this research line of mining semantic information in image has become more attractive, it was not investigated thoroughly. This paper introduces a deep-semantic traffic object recognition based on a knowledge model to reveal relations among detected objects, named D-STOR. In order to confirm the efficiency of the D-STOR framework, an experiment on a dataset of traffic images in Vietnam was conducted and then yielded promising experimental results.

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“…In the phase 3, the reuse of existing ontological resources (e.g. FOAF 3 or OWL Time 4 ) is also clarified.…”

Section: Semantic Reasoning Enginementioning

confidence: 99%

“…With the use of deep learning, not only images can be classified efficiently but visual objects residing inside images can be also recognized correctly [3]. Furthermore, the problem of image recognition has been broadened to recognizing relations between and/or among detected objects.…”

Section: Introductionmentioning

confidence: 99%