Image as Set of Points Article Swipe
Xu Ma
,
Yuqian Zhou
,
Huan Wang
,
Can Qin
,
Bin Sun
,
Chang Liu
,
Yun Fu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.01494
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.01494
What is an image and how to extract latent features? Convolutional Networks (ConvNets) consider an image as organized pixels in a rectangular shape and extract features via convolutional operation in local region; Vision Transformers (ViTs) treat an image as a sequence of patches and extract features via attention mechanism in a global range. In this work, we introduce a straightforward and promising paradigm for visual representation, which is called Context Clusters. Context clusters (CoCs) view an image as a set of unorganized points and extract features via simplified clustering algorithm. In detail, each point includes the raw feature (e.g., color) and positional information (e.g., coordinates), and a simplified clustering algorithm is employed to group and extract deep features hierarchically. Our CoCs are convolution- and attention-free, and only rely on clustering algorithm for spatial interaction. Owing to the simple design, we show CoCs endow gratifying interpretability via the visualization of clustering process. Our CoCs aim at providing a new perspective on image and visual representation, which may enjoy broad applications in different domains and exhibit profound insights. Even though we are not targeting SOTA performance, COCs still achieve comparable or even better results than ConvNets or ViTs on several benchmarks. Codes are available at: https://github.com/ma-xu/Context-Cluster.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.01494
- https://arxiv.org/pdf/2303.01494
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323076583
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4323076583Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.01494Digital Object Identifier
- Title
-
Image as Set of PointsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-02Full publication date if available
- Authors
-
Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun FuList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.01494Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2303.01494Direct 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/2303.01494Direct OA link when available
- Concepts
-
Cluster analysis, Computer science, Artificial intelligence, Pixel, Pattern recognition (psychology), Interpretability, Visualization, Context (archaeology), Feature (linguistics), Geography, Philosophy, Archaeology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.promising | 61 |
| abstract_inverted_index.providing | 155 |
| abstract_inverted_index.targeting | 181 |
| abstract_inverted_index.(ConvNets) | 12 |
| abstract_inverted_index.algorithm. | 89 |
| abstract_inverted_index.clustering | 88, 108, 129, 149 |
| abstract_inverted_index.comparable | 187 |
| abstract_inverted_index.gratifying | 143 |
| abstract_inverted_index.positional | 101 |
| abstract_inverted_index.simplified | 87, 107 |
| abstract_inverted_index.benchmarks. | 198 |
| abstract_inverted_index.information | 102 |
| abstract_inverted_index.perspective | 158 |
| abstract_inverted_index.rectangular | 21 |
| abstract_inverted_index.unorganized | 81 |
| abstract_inverted_index.Transformers | 33 |
| abstract_inverted_index.applications | 168 |
| abstract_inverted_index.convolution- | 122 |
| abstract_inverted_index.interaction. | 133 |
| abstract_inverted_index.performance, | 183 |
| abstract_inverted_index.Convolutional | 10 |
| abstract_inverted_index.convolutional | 27 |
| abstract_inverted_index.coordinates), | 104 |
| abstract_inverted_index.visualization | 147 |
| abstract_inverted_index.attention-free, | 124 |
| abstract_inverted_index.hierarchically. | 118 |
| abstract_inverted_index.representation, | 65, 163 |
| abstract_inverted_index.straightforward | 59 |
| abstract_inverted_index.interpretability | 144 |
| abstract_inverted_index.https://github.com/ma-xu/Context-Cluster. | 203 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |