Retrieval-based multimodal document QA aims to identify and integrate relevant information from visually rich documents with complex multimodal structures. While retrieval-augmented generation (RAG) has shown strong performance in text-based QA, its extensions to multimodal documents remain underexplored and face significant limitations. Specifically, current approaches rely on query-agnostic document representations that overlook salient content and use static top-k evidence selection, which fails to adapt to the uncertain distribution of relevant information. To address these limitations, we propose the Multimodal Adaptive Retrieval-Augmented (MARA) framework, which introduces query-adaptive mechanisms to both retrieval and generation. MARA consists of two components: a Query-Aligned Region Encoder that builds multi-level document representations and reweights them based on query relevance to improve retrieval precision; and a Self-Reflective Evidence Controller that monitors evidence sufficiency during generation and adaptively incorporates content from lower-ranked sources using a sliding-window strategy. Experiments on six multimodal QA benchmarks demonstrate that MARA consistently improves retrieval relevance and answer quality over existing SOTA method.

Referring multi-object tracking (RMOT), which aims to track one or more objects in a video based on a natural language query, is increasingly crucial for a wide range of real-world applications. However, the study of RMOT in satellite video (RMOT-SV) scenarios remains limited, largely due to the high cost of data acquisition and the difficulty of annotation. To address this gap, we introduce RefSat, the first dataset for benchmarking RMOT-SV. RefSat comprises 212 top-down viewpoint video clips, totaling 31,129 frames, collected from a variety of publicly available satellite video datasets. By combining manual annotations of object appearance and position with automatic motion estimation, we build a semi-automatic pipeline that generates high-quality natural language descriptions covering object attributes and motion trajectories, resulting in over 4,000 objects paired with carefully designed textual queries. RefSat features satellite-specific challenges such as small object sizes, cloud occlusions, and motion-referenced semantics. To address these, we introduce RSRefTrack, a tailored baseline designed for small object perception and motion-aware grounding, which outperforms existing state-of-the-art RMOT methods on the RefSat benchmark. Project page: https://github.com/Zhang-Peirong/RefSat

Language-guided remote sensing image object localization uses intuitive natural language interactions to locate objects of interest within satellite or drone imagery, and has a wide range of practical applications. Early research on this task typically used discriminative models that relied on predefined task heads, limited to locating a single object and lacking flexibility. Recent years, multi-modal large language models based generative models leverage their language understanding ability to comprehend more complex language references and provide flexible outputs. However, these models tend to be too large and have limited accuracy in spotting dense, small objects in remote sensing scenarios. The reasons are: 1) Generative models treating continuous coordinate prediction as a token classification problem which fails to reflect the actual localization gap. 2) Current methods typically use CLIP pre-trained encoders that align text-image pairs at a global level, may not capture the fine-grained semantic object information necessary for accurate localization. To address these shortcomings, we introduce a lightweight generative model named LM-RSE (Localization Model with Refined Spotting Enhancement). Refined Spotting Enhancement includes the refinement of bounding box outputs and the integration of fine-grained semantic features. Specifically, we design a Bounding Box Refinement (BBR) approach that includes a special token, a Bounding Box decoder, and a custom regression loss function to refine the spotting precision and optimize the training process. Additionally, we propose a Fine-Grained Semantic Integration (FGSI) strategy, integrates a fine-grained image encoder, a Vision-Language Semantic Processing (VLSP) layer and a two-stage, full-parameter training strategy, all working together to effectively enhance the granularity of spotting. Building on this, we further refine the language-guided object localization task into two types: expression-guided and class-guided. For the former, we utilize the RSVGD dataset for evaluation and achieved state-of-the-art performance; for the latter, our evaluation results surpassed those of GeoChat. Our code and checkpoints will be released at https://github.com/Zhang-Peirong/LM-RSE.