3D Compositional Zero-shot Learning with DeCompositional Consensus Article Swipe
Muhammad Ferjad Naeem
,
Evin Pınar Örnek
,
Yongqin Xian
,
Luc Van Gool
,
Federico Tombari
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.14673
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.14673
Parts represent a basic unit of geometric and semantic similarity across different objects. We argue that part knowledge should be composable beyond the observed object classes. Towards this, we present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes for semantic segmentation. We provide a structured study through benchmarking the task with the proposed Compositional-PartNet dataset. This dataset is created by processing the original PartNet to maximize part overlap across different objects. The existing point cloud part segmentation methods fail to generalize to unseen object classes in this setting. As a solution, we propose DeCompositional Consensus, which combines a part segmentation network with a part scoring network. The key intuition to our approach is that a segmentation mask over some parts should have a consensus with its part scores when each part is taken apart. The two networks reason over different part combinations defined in a per-object part prior to generate the most suitable segmentation mask. We demonstrate that our method allows compositional zero-shot segmentation and generalized zero-shot classification, and establishes the state of the art on both tasks.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.14673
- https://arxiv.org/pdf/2111.14673
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226502236
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4226502236Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.14673Digital Object Identifier
- Title
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3D Compositional Zero-shot Learning with DeCompositional ConsensusWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-29Full publication date if available
- Authors
-
Muhammad Ferjad Naeem, Evin Pınar Örnek, Yongqin Xian, Luc Van Gool, Federico TombariList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.14673Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2111.14673Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2111.14673Direct OA link when available
- Concepts
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Segmentation, Computer science, Artificial intelligence, Object (grammar), Intuition, Point cloud, Zero (linguistics), Generalization, Similarity (geometry), Pattern recognition (psychology), Machine learning, Mathematics, Image (mathematics), Philosophy, Mathematical analysis, Epistemology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.objects. | 12, 78 |
| abstract_inverted_index.observed | 23 |
| abstract_inverted_index.original | 70 |
| abstract_inverted_index.proposed | 60 |
| abstract_inverted_index.semantic | 8, 47 |
| abstract_inverted_index.setting. | 95 |
| abstract_inverted_index.suitable | 160 |
| abstract_inverted_index.Zero-shot | 32 |
| abstract_inverted_index.consensus | 131 |
| abstract_inverted_index.different | 11, 77, 147 |
| abstract_inverted_index.geometric | 6 |
| abstract_inverted_index.intuition | 116 |
| abstract_inverted_index.knowledge | 17 |
| abstract_inverted_index.represent | 1 |
| abstract_inverted_index.solution, | 98 |
| abstract_inverted_index.zero-shot | 170, 174 |
| abstract_inverted_index.Consensus, | 102 |
| abstract_inverted_index.composable | 20 |
| abstract_inverted_index.generalize | 88 |
| abstract_inverted_index.per-object | 153 |
| abstract_inverted_index.processing | 68 |
| abstract_inverted_index.similarity | 9 |
| abstract_inverted_index.structured | 52 |
| abstract_inverted_index.demonstrate | 164 |
| abstract_inverted_index.establishes | 177 |
| abstract_inverted_index.generalized | 173 |
| abstract_inverted_index.benchmarking | 55 |
| abstract_inverted_index.combinations | 149 |
| abstract_inverted_index.segmentation | 84, 107, 123, 161, 171 |
| abstract_inverted_index.Compositional | 31 |
| abstract_inverted_index.compositional | 169 |
| abstract_inverted_index.segmentation. | 48 |
| abstract_inverted_index.generalization | 39 |
| abstract_inverted_index.DeCompositional | 101 |
| abstract_inverted_index.classification, | 175 |
| abstract_inverted_index.Compositional-PartNet | 61 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |