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Добірка наукової літератури з теми "Spatial-semantic model"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 19 липня 2025

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Статті в журналах з теми "Spatial-semantic model"

1

Peng, Huilin, Yang Wang, and Hao Ge. "Spatial-Semantic Transformer for Spatial Relation Recognition." Journal of Physics: Conference Series 2583, no. 1 (2023): 012001. http://dx.doi.org/10.1088/1742-6596/2583/1/012001.

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Abstract Spatial relation recognition, which aims to predict a spatial relation predicate, has attracted increasing attention in the computer vision study. During tackling this problem, modeling spatial relation of the subjects and objects is of great importance. We find that only using spatial features leads to poor results in predicting the spatial relation. To overcome these challenges, we propose an effective spatial attention module to enhance spatial features using semantic features. After identifying the importance of spatial attention mechanism, we propose a spatial transformer module

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2

Abburu, Sunitha. "Geospatial Semantic Query Engine for Urban Spatial Data Infrastructure." International Journal on Semantic Web and Information Systems 15, no. 4 (2019): 31–51. http://dx.doi.org/10.4018/ijswis.2019100103.

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The research aims at design and develop a special semantic query engine “CityGML Spatial Semantic Web Client (CSSWC)” that facilitates ontology-based multicriteria queries on CityGML data in OGC standard. Presently, there is no spatial method, spatial information infrastructure or any tool to establish the spatial semantic relationship between the 3D city objects in CityGML model. The present work establishes the spatial and semantic relationships between the 3DCityObjects and facilitates ontology-driven spatial semantic query engine on 3D city objects, class with multiple attributes, spatial

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3

Wu, Tao, Jianxin Qin, and Yiliang Wan. "TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 410. http://dx.doi.org/10.3390/ijgi8090410.

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To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along netwo

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4

Han, Dongfeng, Wenhui Li, and Zongcheng Li. "Semantic image classification using statistical local spatial relations model." Multimedia Tools and Applications 39, no. 2 (2008): 169–88. http://dx.doi.org/10.1007/s11042-008-0203-6.

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5

Mościcka, Albina. "Europeana Data Model in GIS for movable heritage." Geografie 120, no. 4 (2015): 527–41. http://dx.doi.org/10.37040/geografie2015120040527.

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The paper proposes to use European resources in GIS as a set of multi-spatial objects with semantic relations to the space. It improves the analysis and visualization of geographic or contextual associations between various items. This paper aims to integrate the Europeana Data Model with GIS for movable heritage based on semantic relations of movable objects with the space. All classes and properties of the EDM were analyzed. Classes and properties containing spatial information were examined and their semantic relations to the space were proposed. All aspects of the relations of movable heri

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6

Jia, Chengyou, Minnan Luo, Zhuohang Dang, et al. "SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-Form Layout-to-Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2480–88. http://dx.doi.org/10.1609/aaai.v38i3.28024.

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Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to-Image (L2I) generation, aiming to generate realistic and complex scene images from user-specified layouts, has risen to prominence. However, existing methods transform layout information into tokens or RGB images for conditional control in the generative process, leading to insufficient spatial and semantic controllability of individual instances. To address these limitations, we propose a novel Spatial-Semantic Map

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7

Li, Wenchao, Xin Liu, Chenggang Yan, Guiguang Ding, Yaoqi Sun, and Jiyong Zhang. "STS: Spatial–Temporal–Semantic Personalized Location Recommendation." ISPRS International Journal of Geo-Information 9, no. 9 (2020): 538. http://dx.doi.org/10.3390/ijgi9090538.

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The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations,

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8

Huang, Xinlei, Zhiqi Ma, Dian Meng, et al. "PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 326–33. https://doi.org/10.1609/aaai.v39i1.32010.

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Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of sem

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Wang, Beibei, Youfang Lin, Shengnan Guo, and Huaiyu Wan. "GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4402–9. http://dx.doi.org/10.1609/aaai.v35i5.16566.

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Traffic accident forecasting is of great importance to urban public safety, emergency treatment, and construction planning. However, it is very challenging since traffic accidents are affected by multiple factors, and have multi-scale dependencies on both spatial and temporal dimensional features. Meanwhile, traffic accidents are rare events, which leads to the zero-inflated issue. Existing traffic accident forecasting methods cannot deal with all above problems simultaneously. In this paper, we propose a novel model, named GSNet, to learn the spatial-temporal correlations from geographical an

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10

Shen, Xiang, Dezhi Han, Chongqing Chen, Gaofeng Luo, and Zhongdai Wu. "An effective spatial relational reasoning networks for visual question answering." PLOS ONE 17, no. 11 (2022): e0277693. http://dx.doi.org/10.1371/journal.pone.0277693.

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Visual Question Answering (VQA) is a method of answering questions in natural language based on the content of images and has been widely concerned by researchers. The existing research on the visual question answering model mainly focuses on the point of view of attention mechanism and multi-modal fusion. It only pays attention to the visual semantic features of the image in the process of image modeling, ignoring the importance of modeling the spatial relationship of visual objects. We are aiming at the existing problems of the existing VQA model research. An effective spatial relationship r

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Більше джерел

Дисертації з теми "Spatial-semantic model"

1

Hale, Denise Ann. "The development of semantic memory : a spatial model of animal concepts in schoolchildren, novices and experts." Thesis, London Metropolitan University, 1991. http://repository.londonmet.ac.uk/3390/.

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Cribbin, Timothy Frederick. "Classifying complex topics using spatial-semantic document visualization : an evaluation of an interaction model to support open-ended search tasks." Thesis, Brunel University, 2005. http://bura.brunel.ac.uk/handle/2438/3296.

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In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualizat

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3

Солонская, Светлана Владимировна. "Модели, метод и информационная технология обработки сигналов в интеллектуальных радиолокационных комплексах". Thesis, Харьковский национальный университет радиоэлектроники, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/23588.

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Диссертация на соискание ученой степени кандидата технических наук по специальности 05.13.06 – информационные технологии. – Национальный технический университет "Харьковский политехнический институт", Харьков, 2016. Диссертация посвящена решению научно-практической задачи разработки метода для повышения эффективности обнаружения и распознавания сигналов в радиолокационных комплексах путем интеллектуализации обработки сигнальной информации. В работе проанализированы научные достижения в области обработки сигналов, определены задачи обработки сигналов и подходы к их решению. В технологии обрабо

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4

Солонська, Світлана Володимирівна. "Моделі, метод та інформаційна технологія обробки сигналів в інтелектуальних радіолокаційних комплексах". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/23586.

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Анотація:

Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016. У дисертаційній роботі вирішена науково-практична задача розроблення методу для підвищення ефективності виявлення та розпізнавання сигналів в радіолокаційних комплексах шляхом інтелектуалізації обробки сигнальної інформації. У роботі проаналізовано наукові досягнення в галузі обробки сигналів, визначено задачі обробки сигналів та підходи до їх вирішення. У технології обробки радіолокаційн

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5

Farrugia, James A. "Semantic Interoperability of Geospatial Ontologies: A Model-theoretic Analysis." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/FarrugiaJA2007.pdf.

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Kvasova, Daria 1989. "The Role of cross-modal semantic interactions in real-world visuo-spatial attention." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/668665.

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In our everyday life we must effectively orient attention to relevant objects and events in multisensory environments. The impact of cross-modal links for attention orienting to spatial and temporal cues has been widely described. However, real-life scenarios provide a rich web of semantic information through the different sensory modalities. Despite some previous studies have revealed an impact of crossmodal sematic correspondences, the results are mixed with regard to the conditions in which audiovisual semantic congruence can influence attention orienting. Furthermore, the vast majo

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7

Kang, Hyunmo. "Managing and exploring media using semantic regions a spatial interface supporting user-defined mental models /." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/48.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2003.<br>Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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8

Chen, Xi. "Learning with Sparcity: Structures, Optimization and Applications." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/228.

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The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques p

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CAPOBIANCO, ROBERTO. "Interactive generation and learning of semantic-driven robot behaviors." Doctoral thesis, 2017. http://hdl.handle.net/11573/942393.

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The generation of adaptive and reflexive behavior is a challenging task in artificial intelligence and robotics. In this thesis, we develop a framework for knowledge representation, acquisition, and behavior generation that explicitly incorporates semantics, adaptive reasoning and knowledge revision. By using our model, semantic information can be exploited by traditional planning and decision making frameworks to generate empirically effective and adaptive robot behaviors, as well as to enable complex but natural human-robot interactions. In our work, we introduce a model of semantic m

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Книги з теми "Spatial-semantic model"

1

Regier, Terry. The human semantic potential: Spatial language and constrained connectionism. MIT Press, 1996.

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Regier, Terry. Human Semantic Potential: Spatial Language and Constrained Connectionism. MIT Press, 2019.

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3

Vallar, Giuseppe, and Nadia Bolognini. Unilateral Spatial Neglect. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.012.

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Left unilateral spatial neglect is the most frequent and disabling neuropsychological syndrome caused by lesions to the right hemisphere. Over 50% of right-brain-damaged patients show neglect, while right neglect after left-hemispheric damage is less frequent. Neglect patients are unable to orient towards the side contralateral to the lesion, to detect and report sensory events in that portion of space, as well as to explore it by motor action. Neglect is a multicomponent disorder, which may involve the contralesional side of the body or of extra-personal physical or imagined space, different

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Частини книг з теми "Spatial-semantic model"

1

Harbelot, Benjamin, Helbert Arenas, and Christophe Cruz. "A Semantic Model to Query Spatial–Temporal Data." In Lecture Notes in Geoinformation and Cartography. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31833-7_5.

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2

Ouni, Achref, Thierry Chateau, Eric Royer, Marc Chevaldonné, and Michel Dhome. "A New CBIR Model Using Semantic Segmentation and Fast Spatial Binary Encoding." In Computational Collective Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16014-1_35.

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3

Phan, T. V., G. T. Anh Nguyen, and Trung Tran Do Quoc. "Management of Buildings with Semantic and 3D Spatial Properties by S_EUDM Data Model." In Lecture Notes in Civil Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5144-4_89.

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4

Grisiute, Ayda, Heidi Silvennoinen, Shiying Li, et al. "A Semantic Spatial Policy Model to Automatically Calculate Allowable Gross Floor Areas in Singapore." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37189-9_30.

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5

Koner, Rajat, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, and Stephan Günnemann. "Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering." In The Semantic Web – ISWC 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88361-4_7.

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AbstractVisual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires multi-modal reasoning from both computer vision and natural language processing. We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, and natural language processing techniques. Concretely, our method is based on performing context-driven, sequential reasoning based on the scene entities and their semantic and spatial relationships. As a first step, we derive a scene graph that describes the objects in the image, as well as their attributes and their mutual relationships. Subsequently, a reinforcement learning agent is trained to autonomously navigate in a multi-hop manner over the extracted scene graph to generate reasoning paths, which are the basis for deriving answers. We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs. Our results show that we keep up with human performance on manually curated scene graphs. Moreover, we find that Graphhopper outperforms another state-of-the-art scene graph reasoning model on both manually curated and automatically generated scene graphs by a significant margin.

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6

Wang, Jun, Qinling Dai, Leiguang Wang, Yili Zhao, Haoyu Fu, and Yue Zhang. "High Spatial Resolution Remote Sensing Imagery Classification Based on Markov Random Field Model Integrating Granularity and Semantic Features." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18913-5_39.

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Van Pham, Dang. "Proposing Spatial - Temporal - Semantic Data Model Managing Genealogy and Space Evolution History of Objects in 3D Geographical Space." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67101-3_13.

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Sanderson, Edward, and Bogdan J. Matuszewski. "FCN-Transformer Feature Fusion for Polyp Segmentation." In Medical Image Understanding and Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_65.

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AbstractColonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a result, the use of deep learning (DL) for automation of polyp segmentation has become important. However, DL-based solutions can be vulnerable to overfitting and the resulting inability to generalise to images captured by different colonoscopes. Recent transformer-based architectures for semantic segmentation both achieve higher performance and generalise better than alternatives, however typically predict a segmentation map of $$\frac{h}{4}\times \frac{w}{4}$$ h 4 × w 4 spatial dimensions for a $$h\times w$$ h × w input image. To this end, we propose a new architecture for full-size segmentation which leverages the strengths of a transformer in extracting the most important features for segmentation in a primary branch, while compensating for its limitations in full-size prediction with a secondary fully convolutional branch. The resulting features from both branches are then fused for final prediction of a $$h\times w$$ h × w segmentation map. We demonstrate our method’s state-of-the-art performance with respect to the mDice, mIoU, mPrecision, and mRecall metrics, on both the Kvasir-SEG and CVC-ClinicDB dataset benchmarks. Additionally, we train the model on each of these datasets and evaluate on the other to demonstrate its superior generalisation performance.Code available: https://github.com/CVML-UCLan/FCBFormer.

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9

Hu, Yao, JiaHong Yang, YaQin Wang, and LiuMing Xiao. "Multi-modal Variable-Channel Spatial-Temporal Semantic Action Recognition Network." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8749-4_10.

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10

Kolbe, Thomas H., and Andreas Donaubauer. "Semantic 3D City Modeling and BIM." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_34.

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AbstractSemantic 3D city modeling and building information modeling (BIM) are methods for modeling, creating, and analyzing three-dimensional representations of physical objects of the environment. Digital modeling of the built environment has been approached from at least four different domains: computer graphics and gaming, planning and construction, urban simulation, and geomatics. This chapter introduces the similarities and differences of 3D models from these disciplines with regard to aspects like scale, level of detail, representation of spatial and semantic characteristics, and appearance. Exemplified by the international standards CityGML and Industry Foundation Classes (IFC), information models from semantic 3D city modeling and BIM and their corresponding modeling approaches are explored, and the relationships between them are discussed. Based on use cases from infrastructure planning, approaches for integrating information from semantic 3D city modeling and BIM, such as semantic transformation between CityGML and IFC, are described. Furthermore, the role of semantic 3D city modeling and BIM for recent developments in urban informatics, such as smart cities and digital twins, is investigated and illustrated by real-world examples.

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Тези доповідей конференцій з теми "Spatial-semantic model"

1

Farsijani, Fatemeh, Ali Zaheri, Davar Giveki, and Hossein Peyvandi. "Low-Level Feature Representation in the RCSU-Net Model Using Channel-Spatial Attention Mechanism for Semantic Segmentation of Plant Leaves." In 2025 29th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2025. https://doi.org/10.1109/csicc65765.2025.10967433.

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Wang, Haoxiang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, et al. "SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00367.

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Kent, Lee, Hermenegildo Solheiro, and Keisuke Toyoda. "Multiple Multi-Modal AI for Semantic Annotations of 3D Spatial Data." In 20th International Conference on Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013235300003912.

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Wang, Xiaolin, and Yingwei Luo. "Model semantic network for massive spatial information." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049835.

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Li, Shiqi, Tiejun Zhao, and Hanjing Li. "Improving Spatial Semantic Analysis by a Combining Model." In 2010 International Conference on E-Business and E-Government (ICEE). IEEE, 2010. http://dx.doi.org/10.1109/icee.2010.363.

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Chen, Ying Dong, Rong Guo Chen, Zhen Lin Liu, Zhen Chen, Zhan Wei Lu, and Min Qiang Fan. "Creation of Spatial Information Service Semantic Topology Description Model." In 2009 1st International Conference on Information Science and Engineering (ICISE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.439.

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Memar, Sara, Mohammadreza Ektefa, and Lilly Suriani Affendey. "Developing context model supporting spatial relations for semantic video retrieval." In Knowledge Management (CAMP). IEEE, 2010. http://dx.doi.org/10.1109/infrkm.2010.5466951.

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Yu, Feiyang, and Horace S Ip. "Automatic Semantic Annotation of Images using Spatial Hidden Markov Model." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262459.

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Yan, Y., J. Li, and Z. He. "Research on Ontology Based Semantic Integration Model in Spatial Data Sharing." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.738.

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Wang, Xingang, Kuo Guo, and Zhigang Gai. "A Semi-Formal Multi-Policy Secure Model for Semantic Spatial Trajectories." In the 2017 International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3058060.3058063.

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Звіти організацій з теми "Spatial-semantic model"

1

Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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Анотація:

First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose t

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