Remote sensing (RS) image-text retrieval plays a critical role in understanding massive RS imagery. However, the dense multi-object distribution and complex backgrounds in RS imagery make it difficult to simultaneously achieve fine-grained cross-modal alignment and efficient retrieval. Existing methods either rely on complex cross-modal interactions that lead to low retrieval efficiency, or depend on large-scale vision-language model pre-training, which requires massive data and computational resources. To address these issues, we propose a fast-then-fine (FTF) two-stage retrieval framework that decomposes retrieval into a text-agnostic recall stage for efficient candidate selection and a text-guided rerank stage for fine-grained alignment. Specifically, in the recall stage, text-agnostic coarse-grained representations are employed for efficient candidate selection; in the rerank stage, a parameter-free balanced text-guided interaction block enhances fine-grained alignment without introducing additional learnable parameters. Furthermore, an inter- and intra-modal loss is designed to jointly optimize cross-modal alignment across multi-granular representations. Extensive experiments on public benchmarks demonstrate that the FTF achieves competitive retrieval accuracy while significantly improving retrieval efficiency compared with existing methods.
Northwestern University（西北大学）；Samsung Research America（三星美国研究院）
Learning to answer visual questions is a challenging task since the multi-modal inputs are within two feature spaces. Moreover, reasoning in visual question answering requires the model to understand both image and question, and align them in the same space, rather than simply memorize statistics about the question-answer pairs. Thus, it is essential to find component connections between different modalities and within each modality to achieve better attention. Previous works learned attention weights directly on the features. However, the improvement is limited since these two modality featur…
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing
Institute of Space Earth Science, School of Frontier Sciences, Nanjing University（南京大学空间地球科学学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
organization= Orion Lab, National Observatory of Athens \& National Technical University of Athens , country= Greece；organization= Department of Informatics \& Telematics, Harokopio University of Athens , country= Greece；organization= Faculty of Electrical Engineering；organization= BIFOLD - Berlin Institute for the Foundations of Learning
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学多学科科学先进研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电工程学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；Beijing Institute of Technology（北京理工大学）
SkyNative: A Native Multimodal Architecture for Remote Sensing Vision-Language Understanding
College of Computer Science and Technology, Jilin University, China（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）
Remote Sensing Retrieval-Augmented Generation: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model
School of Cyber Science and Technology, University of Science and Technology of China（信息科学技术学院，中国科学技术大学）；China Academy of Electronics and Information Technology（电子信息技术研究院）
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；Guizhou University（贵州大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shanghai Jiao Tong University（上海交通大学）
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学多学科先进研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电工程学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）
FusionRS: A Large-Scale RGB-Infrared-Style Remote Sensing Dataset for Cross-Modal Vision-Language Learning
China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；University of Electronic Science and Technology of China（电子科技大学）；Tianjin University（天津大学）
With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS. VQA allows a user to formulate a free-form question concerning the content of RS images to extract generic information. It has been shown that the fusion of the input modalities (i.e., image and text) is crucial for the performance of VQA systems. Most of the current fusion approaches use modality-specific representations in their fusion modules instea…
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
Simon Fraser University（西蒙弗雷泽大学）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；Kyoto University（京都大学）；Georgia Institute of Technology（佐治亚理工学院）；National Taiwan Normal University（台湾师范大学）；Purdue University（普渡大学）；Emory University（埃默里大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
JD AI Research（京东人工智能研究院）；Shanghai Jiao Tong University（上海交通大学）
Vision-language pre-training has been an emerging and fast-developing research topic, which transfers multi-modal knowledge from rich-resource pre-training task to limited-resource downstream tasks. Unlike existing works that predominantly learn a single generic encoder, we present a pre-trainable Universal Encoder-DEcoder Network (Uni-EDEN) to facilitate both vision-language perception (e.g., visual question answering) and generation (e.g., image captioning). Uni-EDEN is a two-stream Transformer based structure, consisting of three modules: object and sentence encoders that separately learns …
GeoAlignCLIP: Enhancing Fine-Grained Vision-Language Alignment in Remote Sensing via Multi-Granular Consistency Learning
College of Computer Science and Technology, Jilin University, Changchun 130012, China（吉林大学计算机科学与技术学院，长春130012，中国）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University（教育部符号计算与知识工程重点实验室，吉林大学）
MGIMM: Multi-Granularity Instruction Multimodal Model for Attribute-Guided Remote Sensing Image Detailed Description
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Hubei Luojia Laboratory（湖北珞珈实验室）
Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation. In the recent EEV challenge, a dense affective understanding task is proposed and requires frame-level affective prediction. In this paper, we propose a multi-granularity network with modal attention (MGN-MA), which employs multi-granularity features for better description of the target frame. Specifically, the multi-granularity features could be divided into frame-level, clips-level and video-level features, which corresponds to visual-salient con…
South China University of Technology（华南理工大学）；Baidu VIS（百度视觉技术部）；University of Liverpool（利物浦大学）
Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually fine-grained while textual features are coarse, which is mainly responsible for the large inter-modal gaps. In this paper, we propose an end-to-end framework based on transformers to learn granularity-unified representations for both modal…
Centre of Studies in Resources Engineering (CSRE), Indian Institute of Technology Bombay（印度理工学院孟买分校资源工程研究中心）；German Aerospace Center (DLR)（德国航空航天中心）
Conventional existing retrieval methods in remote sensing (RS) are often based on a uni-modal data retrieval framework. In this work, we propose a novel inter-modal triplet-based zero-shot retrieval scheme utilizing a sketch-based representation of RS data. The proposed scheme performs efficiently even when the sketch representations are marginally prototypical of the image. We conducted experiments on a new bi-modal image-sketch dataset called Earth on Canvas (EoC) conceived during this study. We perform a thorough bench-marking of this dataset and demonstrate that the proposed network outper…
German Aerospace Center（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）；The University of Tokyo（东京大学）；RIKEN（日本理化学研究所）；Wuhan University（武汉大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this…
RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（计算机科学学院，北京邮电大学）；School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China（电子工程学院，北京邮电大学）
UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
DGTRSD & DGTRS-CLIP: A Dual-Granularity Remote Sensing Image-Text Dataset and Vision Language Foundation Model for Alignment
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院 aerospace information research institute）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；School of Information Network Security, People’s Public Security University of China（中国人民公安大学信息网络安全学院）
University of Missouri（密苏里大学）；University of Michigan（密歇根大学）；University of Florida（佛罗里达大学）
In remote sensing, each sensor can provide complementary or reinforcing information. It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data. However, in many remote sensing applications, pixel-level labels are difficult or infeasible to obtain. In addition, outputs from multiple sensors often have different resolution or modalities. For example, rasterized hyperspectral imagery presents data in a pixel grid while airborne Light Detection and Ranging (LiDAR) generates…
University of East Anglia（东英吉利大学）；Newcastle University（纽卡斯尔大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Recognising remote sensing scene images remains challenging due to large visual-semantic discrepancies. These mainly arise due to the lack of detailed annotations that can be employed to align pixel-level representations with high-level semantic labels. As the tagging process is labour-intensive and subjective, we hereby propose a novel Multi-Granularity Canonical Appearance Pooling (MG-CAP) to automatically capture the latent ontological structure of remote sensing datasets. We design a granular framework that allows progressively cropping the input image to learn multi-grained features. For …
CR-JEPA: Cross-Modal Joint-Embedding Predictive Learning for Remote Sensing Image Retrieval
Space Applications Centre, Indian Space Research Organisation（印度空间研究组织空间应用中心）；Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay（印度理工学院孟买资源工程研究中心）
The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., remote sensing image) has attracted great attention in remote sensing (RS). Most of the existing methods assume that a reliable multi-modal training set with accurately matched text-image pairs is existing. However, this assumption may not always hold since the multi-modal training sets may include noisy pairs (i.e., textual descriptions/captions associated to training images can be noisy), distorting the learning …
Space Applications Centre, Indian Space Research Organisation（印度空间研究组织空间应用中心）；Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay（印度理工学院孟买资源工程研究中心）
Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main characters: large intra-class variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two characters. We propose a Multi-granularity Multi-Level Feature Ensemble Network (MGML-FENet) to efficiently tackle RS scene classification task in this paper. Specifically, we propose Multi-granularity Multi-Level Feature Fusion Branch (MGML-FFB) to extract multi-granularity feature…
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
Mila -- Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；Arizona State University（亚利桑那州立大学）；Carleton University（卡尔顿大学）；Allen Institute for AI (Ai2)（人工智能研究所）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）
The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted great attention in remote sensing (RS). In this paper, we focus our attention on cross-modal text-image retrieval, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., image). Most of the existing cross-modal text-image retrieval systems in RS require a high number of labeled training samples and also do not allow fast and memory-efficient retrieval. These issues limit th…
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；Electronic Information School, Wuhan University（电子信息学院，武汉大学）
Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modalities. Existing CM-RSIR methods require the availability of a high quality and quantity of annotated training images. The collection of a sufficient number of reliable labeled images is time consuming, complex and costly in operational scenarios, and can significantly affect the final accuracy of CM-RSIR. In this paper, we introduce a novel self-su…
Towards Comprehensive Interactive Change Understanding in Remote Sensing: A Large-scale Dataset and Dual-granularity Enhanced VLM
Research Center for Space Computing System, Zhejiang Lab（浙江省实验室空间计算系统研究中心）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Cyber Science and Engineering, Zhengzhou University（郑州大学网络科学与工程学院）；Liaoning University of Technology（辽宁科技学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University（武汉测绘遥感与信息工程国家重点实验室（LIESMARS））；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）
SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
Any2Any: Unified Arbitrary Modality Translation for Remote Sensing
National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Wuhan University（多媒体软件国家工程研究中心、人工智能研究院、计算机科学学院、武汉大学）；Hubei Key Laboratory of Multimedia（湖北省多媒体重点实验室）；Zhongguancun Academy, Beijing, China. 100094（中关村学院，北京，中国。100094）；School of Electronic Information, Wuhan University, Wuhan, China（电子信息学院，武汉大学，武汉，中国）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China（测绘、制图与遥感信息工程国家重点实验室，武汉大学，武汉，中国）
