一种用于遥感图像变化描述的轻量级稀疏聚焦Transformer
A Lightweight Sparse Focus Transformer for Remote Sensing Image Change Captioning
AI总结：
本文提出一种轻量级Sparse Focus Transformer（SFT），通过引入稀疏聚焦注意力机制，在保持性能的同时大幅降低Transformer编码器的参数量和计算复杂度，用于遥感图像变化描述任务。
摘要：
遥感图像变化描述（RSICC）旨在自动生成描述遥感双时相图像内容差异的句子。近年来，基于注意力的Transformer已成为捕捉全局变化特征的流行方法。然而，现有的基于Transformer的RSICC方法面临挑战，例如由Transformer编码器组件中的自注意力操作引起的高参数量和高计算复杂度。为了缓解这些问题，本文提出了一种用于RSICC任务的Sparse Focus Transformer（SFT）。具体而言，SFT网络由三个主要组件组成，即基于卷积神经网络（CNN）的高级特征提取器、旨在定位和捕捉双时相图像中变化区域的基于稀疏聚焦注意力机制的Transformer编码器网络，以及嵌入图像和单词以生成描述差异句子的描述解码器。所提出的SFT网络通过在Transformer编码器网络中引入稀疏注意力机制，能够减少参数数量和计算复杂度。在多个数据集上的实验结果表明，即使Transformer编码器的参数和计算复杂度减少了90%以上，我们提出的网络与其他最先进的RSICC方法相比仍能获得有竞争力的性能。代码可在Lite_Chag2cap获取。
英文摘要：
Remote sensing image change captioning (RSICC) aims to automatically generate sentences that describe content differences in remote sensing bitemporal images. Recently, attention-based transformers have become a prevalent idea for capturing the features of global change. However, existing transformer-based RSICC methods face challenges, e.g., high parameters and high computational complexity caused by the self-attention operation in the transformer encoder component. To alleviate these issues, this paper proposes a Sparse Focus Transformer (SFT) for the RSICC task. Specifically, the SFT network consists of three main components, i.e. a high-level features extractor based on a convolutional neural network (CNN), a sparse focus attention mechanism-based transformer encoder network designed to locate and capture changing regions in dual-temporal images, and a description decoder that embeds images and words to generate sentences for captioning differences. The proposed SFT network can reduce the parameter number and computational complexity by incorporating a sparse attention mechanism within the transformer encoder network. Experimental results on various datasets demonstrate that even with a reduction of over 90\% in parameters and computational complexity for the transformer encoder, our proposed network can still obtain competitive performance compared to other state-of-the-art RSICC methods. The code is available at \href{https://github.com/sundongwei/SFT_chag2cap}{Lite\_Chag2cap}.
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。
