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About this paper
Swadhin Das, Vivek Yadav
The Joint Structural–Semantic Fusion Framework (JSSFF) enhances remote sensing image captioning by integrating original and Laplacian edge-aware image features via an early fusion technique and employing a comparison-based beam search decoder. The framework consistently outperformed state-of-the-art baselines across the SYDNEY, UCM, and RSICD datasets, demonstrating improvements in both quantitative metrics and qualitative accuracy.
Key insights
- Early fusion of original and edge-aware image representations is more effective than late fusion, enabling the joint learning of complementary structural and semantic features from the outset for richer image understanding.
- Laplacian edge detection proves to be the most effective among tested methods for capturing fine-grained structural and low-level visual cues, significantly improving object localization and boundary detection in complex scenes.
- Leveraging a comparison-based beam search strategy beyond purely likelihood-driven word selection improves the semantic relevance and contextual accuracy of generated captions, aligning better with human perception.
Summary by alphaXiv.
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