AI 中文总结
针对持续遥感图像-文本检索中跨模态对齐空间失真的问题，提出双适配器与排序感知蒸馏框架DARAD，通过双分支设计与双向排序蒸馏实现新数据适应性与历史数据有效性的平衡，性能优于现有持续学习方法。
AI 中文摘要
随着地球观测技术的快速发展，遥感档案规模迅速扩大，使得遥感图像-文本检索（RS-ITR）的重要性日益凸显。然而，持续RS-ITR仍面临挑战，因为遥感数据中的尺度变化和分布偏移会加剧跨模态对齐空间的失真，导致现有持续学习（CL）方法难以支持可靠的持续检索。为应对这一挑战，我们提出DARAD，这是一种双适配器与排序感知蒸馏框架，可在从不断演化的档案中学习新视觉和文本概念的同时，保留历史跨模态排序结构。具体而言，视觉分支引入空间融合适配器，该适配器整合粗粒度区域线索和细粒度补丁线索，以适应遥感数据的尺度变化，同时将视觉更新锚定到预训练的对齐空间。文本分支采用多专家语义路由，将共享文本语义与语义专门化残差分离，以吸收新出现的描述，同时约束全局文本嵌入的漂移。此外，双向排序蒸馏利用冻结的教师模型和历史锚点来保留历史跨模态排序结构，从而减轻各持续阶段的对齐空间失真。在多阶段持续检索协议下的实验表明，DARAD相较于现有CL方法取得了更优的性能，在提升对新到数据适应性的同时，保持了对历史数据的有效性。
英文摘要
With the rapid growth of Earth observation technologies, remote sensing archives are rapidly expanding, making remote sensing image-text retrieval (RS-ITR) increasingly important. However, continual RS-ITR remains challenging because scale variation and distribution shifts in RS aggravate cross-modal alignment space distortion, making it difficult for existing continual learning (CL) methods to support reliable continual retrieval. To address this challenge, we propose DARAD, a dual-adapter and ranking-aware distillation framework that preserves the historical cross-modal ranking structure while learning new visual and textual concepts from evolving archives. Specifically, the visual branch introduces a spatial fusion adapter, which integrates coarse regional cues and fine-grained patch cues to accommodate RS scale variation while anchoring visual updates to the pretrained alignment space. The textual branch employs multi-expert semantic routing, which separates shared textual semantics from semantically specialized residuals to absorb newly emerging descriptions while constraining global text embedding drift. Furthermore, bidirectional ranking distillation uses a frozen teacher model and historical anchors to preserve the historical cross-modal ranking structure, thereby mitigating alignment space distortion across continual stages. Experiments under a multi-stage continual retrieval protocol show that DARAD achieves superior performance over existing CL methods, improving adaptation to newly arrived data while maintaining effectiveness on historical data.
