用于遥感图像变化描述的渐进式尺度感知网络
Progressive Scale-aware Network for Remote sensing Image Change Captioning
AI总结：
针对遥感图像变化描述中多尺度信息利用不足的问题，提出纯Transformer的PSNet，通过堆叠PDP层并结合SR模块增强解码，实验性能优于已有方法。
摘要：
遥感（RS）图像包含大量不同尺度的目标，这给遥感图像变化描述（RSICC）任务带来了重大挑战：该任务需要在复杂场景中识别感兴趣的视觉变化，并通过语言对其进行描述。然而，当前方法在充分提取和利用多尺度信息方面仍存在一些不足。本文提出一种渐进式尺度感知网络（PSNet）来解决该问题。PSNet是一种纯基于Transformer的模型。为充分提取多尺度视觉特征，研究人员堆叠了多个渐进式差异感知（PDP）层，以逐步挖掘双时相特征的差异特征。为充分利用所提取的多尺度特征生成描述，提出了尺度感知增强（SR）模块，并将其与Transformer解码层相结合，以逐步利用来自不同PDP层的特征。实验表明，PDP层和SR模块均有效，且PSNet优于以往方法。代码已公开：https://github.com/Chen-Yang-Liu/PSNet。
英文摘要：
Remote sensing (RS) images contain numerous objects of different scales, which poses significant challenges for the RS image change captioning (RSICC) task to identify visual changes of interest in complex scenes and describe them via language. However, current methods still have some weaknesses in sufficiently extracting and utilizing multi-scale information. In this paper, we propose a progressive scale-aware network (PSNet) to address the problem. PSNet is a pure Transformer-based model. To sufficiently extract multi-scale visual features, multiple progressive difference perception (PDP) layers are stacked to progressively exploit the differencing features of bitemporal features. To sufficiently utilize the extracted multi-scale features for captioning, we propose a scale-aware reinforcement (SR) module and combine it with the Transformer decoding layer to progressively utilize the features from different PDP layers. Experiments show that the PDP layer and SR module are effective and our PSNet outperforms previous methods. Our code is public at https://github.com/Chen-Yang-Liu/PSNet
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。
