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10.1109/TIP.2021.3092816

In this paper, we present a local semantic enhanced ConvNet (LSE-Net) for aerial scene recognition, which mimics the human visual perception of key local regions in aerial scenes, in the hope of building a discriminative local semantic representation.

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10.24846/V30I1Y202106

The second is instance-based similarity matching using the Semantically enhanced Nearest Neighbour method (SeNN), which is employed in order to compare and quantify the semantic enhanced nearest neighbour entities/labels to predict the exact similarities.

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10.1145/3475731.3484957

Toward this end, we present a novel hierarchical semantic enhanced directional graph network.

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10.2478/cait-2021-0008

To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available.

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10.1145/3347319.3356839

This paper proposes a semantic enhanced encoder-decoder network to tackle this problem.

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10.1109/ICCV.2019.00433

A Semantic Enhanced Network called SeENet is constructed with the parallel pyramid to implement precise segmentation.

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Semantic Enhanced

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Dec 04, 2024

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Semantic Enhanced - In this paper, we present a local semantic enhanced ConvNet (LSE-Net) for aerial scene recognition, which mimics the human visual perception of key local regions in aerial scenes, in the hope of building a discriminative local semantic representation. ^[1] The second is instance-based similarity matching using the Semantically enhanced Nearest Neighbour method (SeNN), which is employed in order to compare and quantify the semantic enhanced nearest neighbour entities/labels to predict the exact similarities. ^[2] Toward this end, we present a novel hierarchical semantic enhanced directional graph network. ^[3] To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. ^[4] This paper proposes a semantic enhanced encoder-decoder network to tackle this problem. ^[5] A Semantic Enhanced Network called SeENet is constructed with the parallel pyramid to implement precise segmentation. ^[6]