How can attention maps be used to improve retrieval performance?
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Attention maps can be used to improve retrieval performance by enhancing the discriminative features in images. Several methods have been proposed to achieve this. One approach is to use attention modules, such as spatial attention and channel attention modules, to adjust the weight distribution of feature maps and make the extracted features more discriminative . Another approach is to employ a query-sensitive co-attention mechanism, which focuses on the salient regions of the image and reduces the computation cost by clustering local features . Additionally, a cross-dimensional attention module can be embedded in a deep hashing network to effectively articulate the high-dimensional semantic content in images . Furthermore, a multi-attention fusion network with dilated convolution and label smoothing can be used to focus on discriminative features and improve retrieval performance . The Attention in Attention (AiA) mechanism, which models inter-dependencies among local and global features, has also been proposed to generate attention values and improve retrieval performance .
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| Papers (4) | Insight |
|---|---|
14 Citations | The paper proposes the Attention in Attention (AiA) mechanism, which uses attention maps to build inter-dependencies among local and global features, improving retrieval performance. |
8 Citations | The paper proposes a multi-attention fusion network that uses attention maps to improve retrieval performance in content-based remote sensing image retrieval. |
| The paper proposes two attention modules, a spatial attention module and a channel attention module, which adjust the weight distribution for the feature map. This makes the extracted features more discriminative and improves retrieval performance. | |
| The paper proposes a query-sensitive co-attention mechanism that uses co-attention maps to improve retrieval performance in content-based image retrieval tasks. |
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