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Context based object categorization: A critical survey
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“Many different types of context have been discussed (Divvala et al. 2009;Galleguillos and Belongie 2010), and can broadly be grouped into one of three categories: 1. Semantic context: The likelihood of an object to be found in some scenes, but not in others; 2. Spatial context: The likelihood of finding an object in some position and not others with respect to other objects in the scene; 3. Scale”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Galleguillos and Belongie 2010
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Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
Yu, F., Koltun, V., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.75 · Cited by 1,937
Relation Networks for Object Detection
Hu, H., Gu, J., Zhang, Z., Dai, J., & Wei, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00378 · Cited by 1,524
Visual Relationship Detection with Language Priors
Lu, C., Krishna, R., Bernstein, M. S., & Fei-Fei, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_51 · Cited by 1,118
Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00611 · Cited by 1,127
Metadata
- Authors
- Galleguillos, C., & Belongie, S.
- Affiliations
- University of California San Diego (US)
- California Institute of Technology (US)
- Corresponding author
- Galleguillos, Carolina
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2010-06
- Volume
- 114
- Issue
- 6
- Pages
- 712-722
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2111868822
- Cited by
- 351
- References
- 41
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 9 · 2023: 8 · 2022: 12 · 2021: 23 · 2020: 26 · 2019: 30 · 2018: 25 · 2017: 27 · 2016: 28 · 2015: 31 · 2014: 35 · 2013: 24 · 2012: 20
- FWCI
- 28.95
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object classification, context modeling, object recognition, contextual information, occlusion, appearance-based recognition, scene statistics, context-aware interaction, scalability
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