Adversarial Robustness of ALIGN-Based Models on RSICD Under Varying Synthetic-to-Real Training Data Ratios

Abstract

Abstract: Remote sensing images contain a wealth of Earth-observation information. Efficient extraction and application of hidden knowledge from these images will greatly promote the development of resource and environment monitoring, urban planning and other related fields. Remote sensing image caption (RSIC) involves obtaining textual descriptions from remote sensing images through accurately capturing and describing the semantic-level relationships between objects and attributes in the images. However, there is currently no comprehensive review summarizing the progress in RSIC based on deep learning.

Research Question

What is the impact of varying the ratio of synthetic to real training data on the adversarial robustness of ALIGN-based models evaluated on RSICD?

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