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Learning consensus-aware semantic knowledge for remote sensing image captioning
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DOI：10.1016/j.patcog.2023.109893.png)
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Abstract
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Tremendous progresses have been made in remote sensing image captioning (RSIC) task in recent years, yet there still some unresolved problems: (1) facing the gap between the visual features and semantic concepts, (2) reasoning the higher-level relationships between semantic concepts. In this work, we focus on injecting high-level visual-semantic interaction into RSIC model. Firstly, the semantic concept extractor (SCE), end-to end trainable, precisely captures the semantic concepts contained in the RSIs. In particular, the visual-semantic co-attention (VSCA) is designed to grain coarse concept-related regions and region-related concepts for multi modal interaction. Furthermore, we incorporate the two types of attentive vectors with semantic-level relational features into a consensus exploitation (CE) block for learning cross-modal consensus-aware knowledge. The experiments on three benchmark data sets show the superiority of our approach compared with the reference methods.
Keywords:
Cross-modal understanding
Visual-semantic interaction
Remote sensing image captioning
Graph convolutional network
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