Long Sun

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.

Referring remote sensing interpretation holds significant application value in various scenarios such as ecological protection, resource exploration, and emergency management. However, referring remote sensing expression comprehension and segmentation (RRSECS) faces critical challenges, including micro-target localization drift problem caused by insufficient extraction of boundary features in existing paradigms. Moreover, when transferred to remote sensing domains, polygon-based methods encounter issues such as contour-boundary misalignment and multi-task co-optimization conflicts problems. In this paper, we propose SeeFormer, a novel contour autoregressive paradigm specifically designed for RRSECS, which accurately locates and segments micro, irregular targets in remote sensing imagery. We first introduce a brain-inspired feature refocus learning (BIFRL) module that progressively attends to effective object features via a coarse-to-fine scheme, significantly boosting small-object localization and segmentation. Next, we present a language-contour enhancer (LCE) that injects shape-aware contour priors, and a corner-based contour sampler (CBCS) to improve mask–polygon reconstruction fidelity. Finally, we develop an autoregressive dual-decoder paradigm (ARDDP) that preserves sequence consistency while alleviating multi-task optimization conflicts. Extensive experiments on RefDIOR, RRSISD, and OPTRSVG datasets under varying scenarios, scales, and task paradigms demonstrate transformative performance gains: compared to the baseline PolyFormer, our proposed SeeFormer improves oIoU and mIoU by 27.58% and 39.37% for referring image segmentation and by 18.94% and 28.90% for visual grounding on the RefDIOR dataset.

Weakly supervised video anomaly detection (WSVAD) aims at predicting frame-level anomaly scores by modeling training videos with video-level annotations. The category names of abnormal events contain high-level knowledge abstracted by humans about abnormalities, which is of great help in identifying abnormal events. To utilize the knowledge implicit in category names, based on the visual-language pretraining model, we introduce a learnable abnormal prompt from three aspects: learnable domain prompt, learnable category prompt, and nonlearnable category definition prompt. Based on the learnable abnormal prompt, we propose a novel fine-grained WSVAD method: PromptVAD, which exploits a learnable abnormal prompt to reduce the semantic gap between visual images and anomaly categories. Through a similarity measure and our proposed coarse-grained two-class prompt module, our PromptVAD jointly learns coarse-grained and fine-grained VAD. Extensive experimental results on the ShanghaiTech, University of Central Florida (UCF)-Crime, and XD-Violence datasets show that our method achieves state-of-the-art performance. Specifically, our method achieves an area under the curve (AUC) of 88.62% on the UCF-Crime dataset.

Remote sensing image-text retrieval (RSITR) is critical for applications, including environmental monitoring and disaster management. The main challenge in this field is that the multi-scale feature of remote sensing images and the semantic differences of professional texts make it difficult to achieve accurate alignment. Existing coarse-grained methods struggle to address the inherent difference between images and text. In light of this, we propose the Fine-Grained Visual-Language Alignment (FGVLA) method. Our FGVLA employs a hybrid loss function that combines coarse-grained contrastive and triplet loss with novel fine-grained loss. Fine-grained loss includes spatial mask loss and fine-grained contrastive loss to enhance semantic alignment. The method also introduces an inference process that works cooperatively with fine-grained loss to explicitly align image patches with textual nouns. Extensive experiments on RSICD, RSITMD, and UCM-Caption datasets demonstrate that FGVLA outperforms existing methods, achieving superior retrieval performance. The code of our FGVLA has been released at https://github.com/Ji-Haoyang/FGVLA.