mlsanityData to 7 Oct 2026

A Survey of Deep Learning for Low-Shot Object Detection

arXiv 2112.02814 ↗First posted 6 Dec 2021cs.CV cs.AIcited by 6

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Abstract (arXiv)

Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task. Low-Shot Object Detection (LSOD) is an emerging research topic of detecting objects from a few or even no annotated samples, consisting of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and analyze them systematically, comprising some extensional topics of LSOD (semi-supervised LSOD, weakly-supervised LSOD, and incremental LSOD). Then, we indicate the pros and cons of current LSOD methods with a comparison of their performance. Finally, we discuss the challenges and promising directions of LSOD to provide guidance for future works.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 91 of its 179 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

91 of 179 references.

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50 most cited of 91 All 91 (JSON)

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2.

6 papers.

  1. 2021Few-Shot Object Detection: A Comprehensive Surveycited by 28
  2. 2022Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparisoncited by 8
  3. 2024Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognitioncited by 6
  4. 2023Improved Region Proposal Network for Enhanced Few-Shot Object Detectioncited by 5
  5. 2023ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentationcited by 2
  6. 2024Beyond Few-shot Object Detection: A Detailed Surveycited by 2