Learning attention-controllable border-ownership for objectness inference and binding Article Swipe
Antoine Dedieu
,
Rajeev Rikhye
,
Miguel Lázaro-Gredilla
,
Dileep George
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2020.12.31.424926
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2020.12.31.424926
Human visual systems can parse a scene composed of novel objects and infer their surfaces and occlusion relationships without relying on object-specific shapes or textures. Perceptual grouping can bind together spatially disjoint entities to unite them as one object even when the object is entirely novel, and bind other perceptual properties like color and texture to that object using object-based attention. Border-ownership assignment, the assignment of perceived occlusion boundaries to specific perceived surfaces, is an intermediate representation in the mammalian visual system that facilitates this perceptual grouping. Since objects in a scene can be entirely novel, inferring border ownership requires integrating global figural information, while dynamically postulating what the figure is, a chicken-and egg process that is complicated further by missing or conflicting local evidence regarding the presence of boundaries. Based on neuroscience observations, we introduce a model – the cloned Markov random field (CMRF)– that can learn attention-controllable representations for border-ownership. Higher-order contour representations that distinguish border-ownerships emerge as part of learning in this model. When tested with a cluttered scene of novel 2D objects with noisy contour-only evidence, the CMRF model is able to perceptually group them, despite clutter and missing edges. Moreover, the CMRF is able to use occlusion cues to bind disconnected surface elements of novel objects into coherent objects, and able to use top-down attention to assign border ownership to overlapping objects. Our work is a step towards dynamic binding of surface elements into objects, a capability that is crucial for intelligent agents to interact with the world and to form entity-based abstractions.
Related Topics
Concepts
Object (grammar)
Artificial intelligence
Clutter
Perception
Computer science
Representation (politics)
Inference
Filling-in
Markov random field
Computer vision
Process (computing)
Pattern recognition (psychology)
Image (mathematics)
Psychology
Image segmentation
Political science
Politics
Telecommunications
Operating system
Neuroscience
Radar
Law
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.12.31.424926
- https://www.biorxiv.org/content/biorxiv/early/2021/01/04/2020.12.31.424926.full.pdf
- OA Status
- green
- Cited By
- 5
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3120252287
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3120252287Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.12.31.424926Digital Object Identifier
- Title
-
Learning attention-controllable border-ownership for objectness inference and bindingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-04Full publication date if available
- Authors
-
Antoine Dedieu, Rajeev Rikhye, Miguel Lázaro-Gredilla, Dileep GeorgeList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.12.31.424926Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/01/04/2020.12.31.424926.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/01/04/2020.12.31.424926.full.pdfDirect OA link when available
- Concepts
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Object (grammar), Artificial intelligence, Clutter, Perception, Computer science, Representation (politics), Inference, Filling-in, Markov random field, Computer vision, Process (computing), Pattern recognition (psychology), Image (mathematics), Psychology, Image segmentation, Political science, Politics, Telecommunications, Operating system, Neuroscience, Radar, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2007015305, https://openalex.org/W2151823074, https://openalex.org/W1967465676, https://openalex.org/W2905304202, https://openalex.org/W2150375089, https://openalex.org/W2057545197, https://openalex.org/W2213060096, https://openalex.org/W2123335490, https://openalex.org/W2390793577, https://openalex.org/W2548549637, https://openalex.org/W2180692697, https://openalex.org/W2109254776, https://openalex.org/W2914105075, https://openalex.org/W2504525781, https://openalex.org/W2257552755, https://openalex.org/W2114542689, https://openalex.org/W2024938108, https://openalex.org/W2135988428, https://openalex.org/W2171590216, https://openalex.org/W1999507572, https://openalex.org/W2270124809, https://openalex.org/W2080920426, https://openalex.org/W2161536125, https://openalex.org/W2160529037, https://openalex.org/W3004792487, https://openalex.org/W2943661400, https://openalex.org/W2994178004, https://openalex.org/W2912871181, https://openalex.org/W3106667718, https://openalex.org/W2137012911, https://openalex.org/W2144701234, https://openalex.org/W2247894828, https://openalex.org/W3084034825, https://openalex.org/W2765332150, https://openalex.org/W3010382778, https://openalex.org/W2133861842, https://openalex.org/W2158570381, https://openalex.org/W2993008448, https://openalex.org/W2112796928, https://openalex.org/W2099877335, https://openalex.org/W1974439923, https://openalex.org/W1987984142, https://openalex.org/W2969424613, https://openalex.org/W3113055895, https://openalex.org/W2115441154, https://openalex.org/W2624780181, https://openalex.org/W1965322047, https://openalex.org/W2095496960, https://openalex.org/W3093868879, https://openalex.org/W1560512119, https://openalex.org/W2144755101, https://openalex.org/W2113291211, https://openalex.org/W2949950285, https://openalex.org/W3104821331, https://openalex.org/W2098385794, https://openalex.org/W1663973292, https://openalex.org/W2157652882, https://openalex.org/W2970952782, https://openalex.org/W2964284952, https://openalex.org/W2962764591, https://openalex.org/W2964176953 |
| referenced_works_count | 61 |
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| abstract_inverted_index.2D | 175 |
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| abstract_inverted_index.or | 24, 122 |
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| abstract_inverted_index.we | 135 |
| abstract_inverted_index.Our | 228 |
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| abstract_inverted_index.for | 151, 246 |
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| abstract_inverted_index.use | 201, 218 |
| abstract_inverted_index.– | 139 |
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| abstract_inverted_index.able | 185, 199, 216 |
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| abstract_inverted_index.cues | 203 |
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| abstract_inverted_index.local | 124 |
| abstract_inverted_index.model | 138, 183 |
| abstract_inverted_index.noisy | 178 |
| abstract_inverted_index.novel | 10, 174, 210 |
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| abstract_inverted_index.parse | 5 |
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| abstract_inverted_index.their | 14 |
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| abstract_inverted_index.figure | 110 |
| abstract_inverted_index.global | 102 |
| abstract_inverted_index.model. | 166 |
| abstract_inverted_index.novel, | 46, 96 |
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| abstract_inverted_index.grouping | 27 |
| abstract_inverted_index.interact | 250 |
| abstract_inverted_index.learning | 163 |
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| abstract_inverted_index.spatially | 31 |
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| abstract_inverted_index.border-ownerships | 158 |
| abstract_inverted_index.attention-controllable | 149 |
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| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101896611 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.59832187 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |