LBTCap：用于实时遥感图像变化描述的轻量级双边变换器
LBTCap: A Lightweight Bilateral Transformer for Real-Time Remote Sensing Image Change Captioning
AI总结：
提出轻量级RSICC框架LBTCap，基于双边变换器联合建模变化前后特征。引入双边注意力机制，结合截断主干和分组查询注意力，参数少且推理速度快，在公共数据集实验中实现精度与效率平衡。
摘要：
遥感图像变化描述（RSICC）生成配对遥感图像间语义变化的自然语言描述。虽近期方法 captioning 精度高，但大多忽视计算效率和推理速度。为此提出LBTCap，基于双边Transformer的轻量级RSICC框架，联合建模变化前后特征。引入双边注意力机制，结合截断主干和分组查询注意力，参数仅39.99M，变化感知编码器仅占2.78M。在两个公共RSICC数据集上的大量实验表明，LBTCap在使用更少参数和更高推理速度的情况下，匹配或接近最先进方法的精度，双边公式在低资源设置中优势明显，为实际RSICC展示了良好的精度-效率权衡。
英文摘要：
Remote sensing image change captioning (RSICC) generates natural-language descriptions of semantic changes between paired remote sensing images (RSIs), supporting applications such as urban planning, disaster response, and environmental monitoring. Although recent methods achieve strong captioning accuracy, most overlook computational efficiency and inference speed, which are essential for real-time deployment in practice. To this end, we propose LBTCap, a lightweight RSICC framework built on a bilateral Transformer that jointly models pre- and post-change features for efficient processing of paired RSIs. Specifically, we introduce a bilateral attention mechanism for paired inputs: the two temporal images are projected into separate queries and keys by the same query and key matrices shared across the two images, the value is formed from their concatenation, and the two resulting attention maps are combined by a learnable, structurally bilateral weighting instead of a fixed subtraction. This design keeps both temporal branches explicit while remaining compact, and, together with a truncated backbone and grouped-query attention, LBTCap uses only 39.99M parameters, of which the change-aware encoder accounts for just 2.78M. Extensive experiments on two public RSICC datasets show that LBTCap matches or closely approaches the accuracy of state-of-the-art methods while using far fewer parameters and running at markedly higher inference speed, with the benefit of the bilateral formulation most pronounced in the low-resource setting, demonstrating a favorable accuracy-efficiency trade-off for practical RSICC.
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