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Cited by 100Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

A Survey of Self-Supervised and Few-Shot Object Detection

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Huang, Gabriel, Laradji, Issam, Vazquez, David, Lacoste-Julien, Simon, Rodriguez, Pau·2022·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2022.3199617
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Abstract

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.

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  • There is a clear lack of systematic and comprehensive surveys specifically on FSCIL.

    “Despite existing surveys on FSL [27, 28, 29, 30] and IL [5, 6, 7, 11, 31, 32, 33, 34, 35], there is a clear lack of systematic and comprehensive surveys specifically on FSCIL.”

    From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [30]

  • Existing FSL surveys do not touch upon the issue of FSCIL.

    “Existing FSL surveys primarily focus on tackling ML problems with limited data. While they provide systematic classifications of FSL methods, they do not touch upon the issue of FSCIL.”

    From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [30]

    Section 4.2, “Important Differences with Few-Shot Classification” (page not available), compares few-shot classification with few-shot object detection but does not assess what existing FSL surveys cover. Searches for FSCIL, few-shot class-incremental learning, and survey coverage found no passage supporting the claim that those surveys omit FSCIL. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Huang, G., Laradji, I., Vazquez, D., Lacoste-Julien, S., & Rodriguez, P.
Affiliations
  • Mila - Quebec Artificial Intelligence Institute (CA)
  • ServiceNow (United States) (US)
  • ServiceNow, Canada
ORCID
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
45
Issue
4
Pages
1-20
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
  • 1939-3539
DOI
10.1109/tpami.2022.3199617
OpenAlex
W3208441708
Cited by
100
References
11
Citations by year
2026: 7 · 2025: 29 · 2024: 33 · 2023: 23 · 2022: 7 · 2021: 1
FWCI
7.57
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Canadian Institute for Advanced Research
  • Mitacs
  • Natural Sciences and Engineering Research Council of Canada (rgpin-2017-06936)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
few-shot object detection, self-supervised learning, object detection, instance segmentation, representation learning, unlabeled data, transfer learning

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