Dimensions overall score 7.0

• The proposed RRSITR method significantly outperforms state-of-the-art methods in remote sensing image-text retrieval, especially under high noise rates.result 95%
• Remote sensing datasets like RSITMD contain inaccurate or mismatched image-text descriptions, revealing a noisy correspondence (NC) problem in RSITR.limitation 90%
• RRSITR uses a self-paced learning strategy that divides training pairs into clean, ambiguous, and noisy categories based on loss magnitude to learn from easy to hard.method 90%
• The method assigns a reliability weight to each training pair based on loss values to dynamically regulate the training sequence and sample weights.method 85%
• A robust triplet loss is presented for noisy sample pairs to dynamically adjust the soft margin based on semantic similarity, enhancing robustness against noise.technical 85%
• Experimental evaluation uses synthetic noise introduced by randomly shuffling text descriptions across images at controlled rates (20%, 40%, 60%, 80%) to simulate real-world misalignment.method 90%
• The proposed method outperforms specific state-of-the-art methods including CUP, MSA, and SIRS across multiple benchmark datasets (RSITMD, RSICD, NWPU).result 95%
• Noisy correspondence (NC) is an important but previously untouched problem in remote sensing image-text retrieval research.technical 80%

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