Xiaowen Zhang

Recent advancements in natural image visual grounding have shown promising results. However, existing approaches struggle with precise grounding in remote sensing (RS) images. This challenge arises from two key issues. First, the prevalence of small objects and dense spatial distributions in RS images makes accurate localization difficult. Second, the large domain gap and lack of contextual priors in RS data hinder language-vision alignment. In this paper, we propose RefineVG, a novel framework for fine-grained visual grounding in high-resolution RS images. Unlike methods that require training separate models for coarse localization and fine-grained refinement, RefineVG employs a unified model that iteratively refines predictions. Specifically, RefineVG first predicts a coarse bounding box using a multi-scale cross-modality encoder and a Transformer-based decoder, which progressively align and fuse visual and textual feature. Then, the predicted region is enlarged to capture additional contextual feature. Combined with only category information as text, the cropped region is then re-encoded by the same model to obtain a refined grounding result. Finally, a fusion block combines the coarse and refined predictions. To support this process, we propose a data augmentation strategy by randomly expanding ground truth bounding boxes in training dataset, simulating spatial uncertainty.Extensive experiment results show that RefineVG achieves outstanding performance on three benchmarks, outperforming the second-best method by 5.11% on the DIOR-RSVG dataset, 8.61% on the OPT-RSVG dataset, and 3.61% on the RSVG-HR dataset in P@0.9. Our code are available at https://github.com/XiaowZhang00/RefineVG.

Remote sensing visual grounding (RSVG) models commonly rely on backbones pretrained on natural image datasets. However, the inherent domain gap between natural images and remote sensing imagery significantly degrades model transferability, particularly when RSVG training samples are limited. Existing approaches primarily focus on enhancing localization accuracy through multi-scale visual–textual alignment and process each image–text pair independently, which limits their generalization capability under constrained supervision. In this work, we propose VisproVG, a novel framework that explicitly incorporates category-aware visual priors into the linguistic stream to mitigate domain shift and data scarcity. Specifically, VisproVG introduces a sample-level visual prompting mechanism that retrieves instance-level visual cues from a dynamically maintained, category-specific memory bank, which serve as stable and interpretable visual priors. To further improve semantic consistency across feature scales, we incorporate a lightweight cross-scale semantic refinement module that performs hierarchical top–down refinement. Beyond architectural design, we conduct a systematic analysis of the relationship between decoder confidence and localization accuracy. The analysis indicates that low-confidence predictions, while accounting for only a small fraction of test samples, exhibit substantially greater potential for improvement. Motivated by this observation, we introduce a confidence-guided adaptive test-time augmentation (TTA) strategy that selectively applies augmentation to uncertain samples, achieving a favorable balance between accuracy gains and computational efficiency. Extensive experiments on three RSVG benchmarks demonstrate the effectiveness of the proposed VisproVG. Notably, on the small-scale RSVG-HR dataset, VisproVG surpasses the second-best method by 5.05% meanIoU, highlighting its robustness under limited training data. Our code are available at https://github.com/XiaowZhang00/VisproVG.

Change detection (CD) based on deep learning has achieved excellent performance in recent years. However, these models exhibit limited capability in complete temporal modeling or face problems with fine-grained spatial features being overshadowed by the temporal context. Pure CNN-based CD pipelines also struggle to establish long-range connections. In this paper, a globally-aware continuous-time redistribution network (GCRNet) is proposed for RSCD. First, a boundary extraction branch is designed to preserve the semantic invariance of objects within the same boundary. This is achieved by providing boundary attention to adaptively guide the integration of temporal and spatial information. Then, a globally-aware operator (GAO) is developed to obtain global interaction features. GAO utilizes the convolution theorem, which combines Fourier transform and inverse Fourier transform, achieving it with low computational costs. Finally, an adaptive feature redistribution (AFR) module is designed to increase the distance between positive and negative samples in the latent space with change perception. It alleviates the effects of the severe class imbalance issue. Experimental results demonstrate that our proposed GCRNet surpasses 13 state-of-the-art CD methods. It achieves F1-score 0.33%, 0.62%, 0.84%, 0.17%, and 1.54% higher than the second-best model on the LEVIR-CD, LEVIR-CD+, WHU, CDD, and DSIFN datasets. The code of GCRNet is available at https://github.com/XiaowenZhang-kuku/GCRNet.