Abstracting Sketches through Simple Primitives Article Swipe
Stephan Alaniz
,
Massimiliano Mancini
,
Anjan Dutta
,
Diego Marcos
,
Zeynep Akata
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.13543
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.13543
Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and communicate them in an interpretable protocol. Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primitives under the influence of a budget. To solve this task, our Primitive-Matching Network (PMN), learns interpretable abstractions of a sketch in a self supervised manner. Specifically, PMN maps each stroke of a sketch to its most similar primitive in a given set, predicting an affine transformation that aligns the selected primitive to the target stroke. We learn this stroke-to-primitive mapping end-to-end with a distance-transform loss that is minimal when the original sketch is precisely reconstructed with the predicted primitives. Our PMN abstraction empirically achieves the highest performance on sketch recognition and sketch-based image retrieval given a communication budget, while at the same time being highly interpretable. This opens up new possibilities for sketch analysis, such as comparing sketches by extracting the most relevant primitives that define an object category. Code is available at https://github.com/ExplainableML/sketch-primitives.
Related Topics
Concepts
Sketch
Computer science
Abstraction
Set (abstract data type)
Sketch recognition
Object (grammar)
Affine transformation
Task (project management)
Matching (statistics)
Code (set theory)
Simple (philosophy)
Artificial intelligence
Theoretical computer science
Abstraction layer
Protocol (science)
Programming language
Information retrieval
Algorithm
Software
Mathematics
Statistics
Management
Gesture
Economics
Pathology
Philosophy
Alternative medicine
Pure mathematics
Medicine
Epistemology
Gesture recognition
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.13543
- https://arxiv.org/pdf/2207.13543
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4288723497
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4288723497Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2207.13543Digital Object Identifier
- Title
-
Abstracting Sketches through Simple PrimitivesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-27Full publication date if available
- Authors
-
Stephan Alaniz, Massimiliano Mancini, Anjan Dutta, Diego Marcos, Zeynep AkataList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.13543Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2207.13543Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2207.13543Direct OA link when available
- Concepts
-
Sketch, Computer science, Abstraction, Set (abstract data type), Sketch recognition, Object (grammar), Affine transformation, Task (project management), Matching (statistics), Code (set theory), Simple (philosophy), Artificial intelligence, Theoretical computer science, Abstraction layer, Protocol (science), Programming language, Information retrieval, Algorithm, Software, Mathematics, Statistics, Management, Gesture, Economics, Pathology, Philosophy, Alternative medicine, Pure mathematics, Medicine, Epistemology, Gesture recognitionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
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| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
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| primary_location.version | submittedVersion |
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| publication_date | 2022-07-27 |
| publication_year | 2022 |
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| abstract_inverted_index.is | 45, 122, 128, 186 |
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| cited_by_percentile_year | |
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| institutions_distinct_count | 5 |
| citation_normalized_percentile |