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Suping Wang, Ligu Zhu, Lei Shi, Hao Mo and Songfu Tan
Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep repres...
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Hui Luo, Jiamin Li, Lianming Cai and Mingquan Wu
Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model ...
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Wenxiao Li, Hongyan Mei, Yutian Li, Jiayao Yu, Xing Zhang, Xiaorong Xue and Jiahao Wang
Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the existin...
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James Durham, Sudipta Chowdhury and Ammar Alzarrad
Effectively harnessing the power of social media data for disaster management requires sophisticated analysis methods and frameworks. This research focuses on understanding the contextual information present in social media posts during disasters and dev...
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Anita Agárdi and László Kovács
The development of an appropriate ontology model is usually a hard task. One of the main issues is that ontology developers usually concentrate on classes and neglect the role of properties. This paper analyzes the role of an appropriate property set in ...
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