Open-vocabulary segmentation (OVS) allows models to segment any categories based on text prompts, overcoming the limitations of traditional closed-set methods. Open-vocabulary segmentation (OVS) in remote sensing is limited by its reliance on standard RGB images. This prevents models from using valuable information from other spectral bands, such as Near-Infrared (NIR). This paper proposes MovSeg, a framework for multispectral open-vocabulary segmentation. Our method efficiently integrates 4-band (RGB+NIR) data into a pre-trained vision-language model. MovSeg introduces two components: a Multispectral Input Adaptation (MIA) module and a Spatial-Channel Adaptive Tuning (SCAT) strategy. The MIA module adapts the input layer to process 4-band data. It uses a weight-copying strategy for initialization and a WindowECA mechanism to re-weight spectral channels. The SCAT strategy is a parameter-efficient strategy that uses a hybrid of LoRA and Adapters to fine-tune deep features with low computational cost. Experiments on three multispectral datasets show that MovSeg outperforms existing general-domain and remote sensing OVS methods. The model achieves significant gains on NIR-sensitive classes, confirming its ability to exploit the extra spectral data. The codes will be coming soon.

The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-based methodologies have made progress, they are fundamentally limited by treating damages as independent, isolated entities, thereby ignoring the intrinsic spatial symmetry and topological organization inherent in complex damage patterns like alligator cracking. This conceptual asymmetry in modeling leads to two major deficiencies: “context blindness,” which overlooks essential structural interrelations, and “temporal inconsistency” in video analysis, resulting in unstable, flickering predictions. To address this, we propose a Spatio-Temporal Graph Mamba You-Only-Look-Once (STG-Mamba-YOLO) network, a novel architecture that introduces a symmetry-informed, hierarchical reasoning process. Our approach explicitly models and integrates contextual dependencies across three levels to restore a holistic and consistent structural representation. First, at the pixel level, a Mamba state-space model within the YOLO backbone enhances the modeling of long-range spatial dependencies, capturing the elongated symmetry of linear cracks. Second, at the object level, an intra-frame damage Graph Network enables explicit reasoning over the topological symmetry among damage candidates, effectively reducing false positives by leveraging their relational structure. Third, at the sequence level, a Temporal Graph Mamba module tracks the evolution of this damage graph, enforcing temporal symmetry across frames to ensure stable, non-flickering results in video streams. Comprehensive evaluations on multiple public benchmarks demonstrate that our method outperforms existing state-of-the-art approaches. STG-Mamba-YOLO shows significant advantages in identifying intricate damage topologies while ensuring robust temporal stability, thereby validating the effectiveness of our symmetry-guided, multi-level contextual fusion paradigm for structural health monitoring.

针对深度学习在多光谱遥感应用中面临的数据获取困难与标注成本高昂问题，以及现有基础生成模型难以直接适配多光谱数据且从零训练计算开销巨大的现状，提出了一种面向多光谱遥感图像生成的参数高效适配扩散模型。该方法采用参数高效微调策略，通过在冻结的预训练扩散模型中嵌入各种低参数微调模块，不同于通用可控生成方法仅以数据驱动方式建模图像，本文在适配训练中显式引入遥感光谱物理约束，并针对地物语义-空间映射设计了文本感知编码机制。实现了从RGB图像域向四波段（RGB+NIR）图像域的低成本迁移，不同微调模块综合了光谱与空间纹理适配。在此基础上，引入基于归一化植被指数（Normalized Difference Vegetation Index， NDVI）和归一化水体指数（Normalized Difference Water Index， NDWI）的物理一致性损失，强制约束红光与近红外波段间的光谱相关性。此外，提出文本感知空间语义编码机制，利用语义分割掩膜实现对地物空间布局的精确控制。在FLAIR、Five-Billion-Pixels及IRSAMap等数据集上的实验表明，与ControlNet、T2I-Adapter等主流方法相比，本文方法在光谱保真度与语义对齐度上均有所提升，生成的近红外波段具备明确的物理意义。此外，利用生成数据辅助训练在下游开放词汇分割任务上取得了一定的精度提升，验证了该方法作为数据增强手段的可行性。本框架有效解决了RGB基础模型向多光谱遥感领域迁移时的通道不匹配与物理特征丢失问题，实现了低资源消耗下的高质量、可控多光谱数据生成，为缓解遥感解译任务中的数据稀缺问题提供了有效的数据增强方案。

Financial question answering is often treated as document question answering, although financial evidence is both multimodal and time-dependent. Semantically equivalent facts expressed in narrative text, tables, page images, or Extensible Business Reporting Language (XBRL) should support consistent answers, whereas a disclosure may support a query only after becoming public. We formalise this combination as crossmodal evidence symmetry under a causal temporal boundary and introduce TempFinRAG, a multimodal temporal retrieval-augmented generation (RAG) framework for point-in-time financial question answering. Given a question, company, and as-of date, the framework enforces the information boundary defined by U.S. Securities and Exchange Commission (SEC) filing availability; aligns page layout, text, table structure, and XBRL facts; retrieves time-valid evidence; executes auditable financial calculations; and generates a cited answer. A verifier checks temporal validity, claim support, numerical consistency, and the need to abstain. We further introduce TempFinQA, a point-in-time evaluation protocol built from public filings and XBRL facts, and evaluate the framework on complementary evidence-grounded, numerical, conversational, and multi-table benchmarks. On TempFinQA, TempFinRAG improves answer accuracy from 66.7% to 78.9% over hybrid RAG while reducing temporal evidence leakage from 10.8% to 1.7% and hallucination from 17.3% to 7.9%. Reliable financial question answering therefore requires consistent treatment across evidence representations and deliberately asymmetric access across time.