Visual Object Recognition
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The research reviewed in Grauman and Leibe (2011), Andreopoulos and Tsotsos (2013) and Zhang et al. (2013) is mostly pre-2012, prior to the recent success and dominance of deep learning.
“However, the research reviewed in Grauman and Leibe (2011), Andreopoulos and Tsotsos (2013) and Zhang et al. (2013) is mostly pre-2012, and therefore prior to the recent striking success and dominance of deep learning and related methods.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Grauman and Leibe 2011
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- Authors
- Grauman, K., & Leibe, B.
- Venue
- Synthesis Lectures on Artificial Intelligence and Machine Learning
- Publisher
- Springer International Publishing
- Published
- 2011
- Volume
- 5
- Issue
- 2
- Pages
- 1-181
- ISSN
- 1939-4608 (ISSN-L)
- 1939-4616
- OpenAlex
- W2162250601
- Cited by
- 126
- References
- 275
- Citations by year
- 2023: 5 · 2022: 3 · 2021: 11 · 2020: 12 · 2019: 20 · 2018: 11 · 2017: 9 · 2016: 15 · 2015: 14 · 2014: 12 · 2013: 11 · 2012: 3
- FWCI
- 8.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Synthesis lectures on artificial intelligence and machine learning (publishedVersion)
- RWTH Publications (RWTH Aachen) (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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- visual object recognition, image classification, visual categorization, object localization, image representation, learning methods, computer vision algorithms, robotics, information retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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