Search Results - "Lv, Guangrui"

Novel Asymmetric Pyramid Aggregation Network for Infrared Dim and Small Target Detection by Lv, Guangrui, Dong, Lili, Liang, Junke, Xu, Wenhai

“…Robust and efficient detection of small infrared target is a critical and challenging task in infrared search and tracking applications. The size of the small…”
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Hierarchical interactive multi-granularity co-attention embedding to improve the small infrared target detection by Lv, Guangrui, Dong, Lili, Xu, Wenhai

“…Small Infrared Target Detection (SITD) aims to separate the information of small targets from complex background clutter. Infrared radiation decays with…”
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Multi-scale attentive region adaptive aggregation learning for remote sensing scene classification by Lv, Guangrui, Dong, Lili, Zhang, Wenwen, Xu, Wenhai

“…Remote sensing scene classification (RSSC) is an active topic in the field of remote sensing and has attracted a lot of attention due to its wide range of…”
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Region-based adaptive association learning for robust image scene recognition by Lv, Guangrui, Dong, Lili, Zhang, Wenwen, Xu, Wenhai

“…Scene recognition is challenging due to the complicated spatial arrangement and varied object distribution inside the scene images. Deep learning methods,…”
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Aspect-level multimodal sentiment analysis based on co-attention fusion by Wang, Shunjie, Cai, Guoyong, Lv, Guangrui

“…Aspect-level multimodal sentiment analysis is the fine-grained sentiment analysis task of predicting the sentiment polarity of given aspects in multimodal…”
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A global-local feature adaptive fusion network for image scene classification by Lv, Guangrui, Dong, Lili, Zhang, Wenwen, Xu, Wenhai

“…Convolutional neural networks (CNN) have been widely used in image scene classification and have achieved remarkable progress. However, because the extracted…”
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Heterogeneous Transfer with Deep Latent Correlation for Sentiment Analysis by Cai, Guoyong, Lv, Guangrui

“…Most traditional methods of image sentiment analysis focus on the design of visual features, and the usefulness of texts associated to the images have not been…”
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