Exploring spatial reasoning performances of CNN on linear layout dataset
Spatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limit...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad9706 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850118027591286784 |
|---|---|
| author | Jelena Pejic Marko Petkovic Sandra Klinge |
| author_facet | Jelena Pejic Marko Petkovic Sandra Klinge |
| author_sort | Jelena Pejic |
| collection | DOAJ |
| description | Spatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limits the development and evaluation of models that can effectively handle spatial reasoning tasks. Existing datasets often contain complex spatial reasoning problems with overlapping spatial relationships, making it challenging to diagnose specific aspects that a model struggles with. We address this gap by introducing a new dataset of linear layouts. This dataset is systematically designed to exhibit a range of spatial relations and complexity levels. Analyzing spatial reasoning through linear layout generation offers a more structured and manageable approach to understanding how models learn and interpret spatial relationships. Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. Experimental results reveal that LinLayCNN can effectively solve fundamental spatial challenges even with the relatively small size of the training set. It is capable of precise object placement, making it a robust tool for linear layout generation. Current layout generation methods focus on domain-specific solutions and often fail to maintain the precision needed for technical domains, such as accurate sizing, and object counting. They also require a substantial amount of data to function effectively. LinLayCNN overcame these issues. This study further clarifies CNNs’ capabilities in spatial reasoning, highlight their potential to advance the field of layout generation. As a result, our approach establishes a clear benchmark for evaluating spatial reasoning and aids in development of models that can more effectively understand and reason about space. |
| format | Article |
| id | doaj-art-fd1c2a71beef4ce6964c0d88d2244f36 |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-fd1c2a71beef4ce6964c0d88d2244f362025-08-20T02:35:57ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015404505610.1088/2632-2153/ad9706Exploring spatial reasoning performances of CNN on linear layout datasetJelena Pejic0https://orcid.org/0000-0002-0451-7467Marko Petkovic1https://orcid.org/0000-0002-6862-1968Sandra Klinge2https://orcid.org/0000-0003-2620-8291Computer Science Department, Faculty of Sciences and Mathematics, University of Nis , Nis, SerbiaComputer Science Department, Faculty of Sciences and Mathematics, University of Nis , Nis, SerbiaDepartment Structural Mechanics and Analysis, TU Berlin , Berlin, GermanySpatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limits the development and evaluation of models that can effectively handle spatial reasoning tasks. Existing datasets often contain complex spatial reasoning problems with overlapping spatial relationships, making it challenging to diagnose specific aspects that a model struggles with. We address this gap by introducing a new dataset of linear layouts. This dataset is systematically designed to exhibit a range of spatial relations and complexity levels. Analyzing spatial reasoning through linear layout generation offers a more structured and manageable approach to understanding how models learn and interpret spatial relationships. Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. Experimental results reveal that LinLayCNN can effectively solve fundamental spatial challenges even with the relatively small size of the training set. It is capable of precise object placement, making it a robust tool for linear layout generation. Current layout generation methods focus on domain-specific solutions and often fail to maintain the precision needed for technical domains, such as accurate sizing, and object counting. They also require a substantial amount of data to function effectively. LinLayCNN overcame these issues. This study further clarifies CNNs’ capabilities in spatial reasoning, highlight their potential to advance the field of layout generation. As a result, our approach establishes a clear benchmark for evaluating spatial reasoning and aids in development of models that can more effectively understand and reason about space.https://doi.org/10.1088/2632-2153/ad9706machine learninglayout designlinear layoutconvolutional neural networkspatial reasoningspatial relations |
| spellingShingle | Jelena Pejic Marko Petkovic Sandra Klinge Exploring spatial reasoning performances of CNN on linear layout dataset Machine Learning: Science and Technology machine learning layout design linear layout convolutional neural network spatial reasoning spatial relations |
| title | Exploring spatial reasoning performances of CNN on linear layout dataset |
| title_full | Exploring spatial reasoning performances of CNN on linear layout dataset |
| title_fullStr | Exploring spatial reasoning performances of CNN on linear layout dataset |
| title_full_unstemmed | Exploring spatial reasoning performances of CNN on linear layout dataset |
| title_short | Exploring spatial reasoning performances of CNN on linear layout dataset |
| title_sort | exploring spatial reasoning performances of cnn on linear layout dataset |
| topic | machine learning layout design linear layout convolutional neural network spatial reasoning spatial relations |
| url | https://doi.org/10.1088/2632-2153/ad9706 |
| work_keys_str_mv | AT jelenapejic exploringspatialreasoningperformancesofcnnonlinearlayoutdataset AT markopetkovic exploringspatialreasoningperformancesofcnnonlinearlayoutdataset AT sandraklinge exploringspatialreasoningperformancesofcnnonlinearlayoutdataset |