一种用于遥感图像描述的边缘感知融合新型轻量级 Transformer
A Novel Lightweight Transformer with Edge-Aware Fusion for Remote Sensing Image Captioning
AI总结：
针对遥感图像描述模型计算成本高且忽视边缘等细粒度结构的问题，本文提出结合 GPT-2 蒸馏解码器、知识蒸馏与边缘感知增强的轻量级 Transformer，显著提升描述质量。
摘要：
基于 Transformer 的模型通过捕获长程依赖关系和上下文信息，在遥感图像描述任务中取得了优异性能。然而，其高昂的计算成本阻碍了实际部署，尤其是在采用独立的基于 Transformer 的编码器和解码器的多模态框架中。此外，现有遥感图像描述模型主要关注高级语义提取，却往往忽视边缘、轮廓和物体边界等细粒度结构特征。为应对这些挑战，本文提出一种轻量级 Transformer 架构，通过降低编码器层的维度，并采用 GPT-2 的蒸馏版本作为解码器。该方法使用知识蒸馏策略，将更复杂的教师模型中的知识迁移过来，以提升轻量级网络的性能。此外，本文还引入边缘感知增强策略，以增强图像表示和物体边界理解，使模型能够捕获遥感图像中的细粒度空间细节。实验结果表明，与最先进方法相比，所提方法显著提升了描述质量。
英文摘要：
Transformer-based models have achieved strong performance in remote sensing image captioning by capturing long-range dependencies and contextual information. However, their practical deployment is hindered by high computational costs, especially in multi-modal frameworks that employ separate transformer-based encoders and decoders. In addition, existing remote sensing image captioning models primarily focus on high-level semantic extraction while often overlooking fine-grained structural features such as edges, contours, and object boundaries. To address these challenges, a lightweight transformer architecture is proposed by reducing the dimensionality of the encoder layers and employing a distilled version of GPT-2 as the decoder. A knowledge distillation strategy is used to transfer knowledge from a more complex teacher model to improve the performance of the lightweight network. Furthermore, an edge-aware enhancement strategy is incorporated to enhance image representation and object boundary understanding, enabling the model to capture fine-grained spatial details in remote sensing images. Experimental results demonstrate that the proposed approach significantly improves caption quality compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
