Few-Shot Object Detection: Research Advances and Challenges

Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object category to ensure accurate detection, but obtaining extensive annotated data is a labor-intensive and expensive process in many real-world scenarios. To tackle this challenge, researchers have explored few-shot object detection (FSOD) that combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions. Specifically, we first introduce the background and definition of FSOD to emphasize potential value in advancing the field of computer vision. We then propose a novel FSOD taxonomy method and survey the plentifully remarkable FSOD algorithms based on this fact to report a comprehensive overview that facilitates a deeper understanding of the FSOD problem and the development of innovative solutions. Finally, we discuss the advantages and limitations of these algorithms to summarize the challenges, potential research direction, and development trend of object detection in the data scarcity scenario.

• Publication year

  2024

• Venue

  Information Fusion

• Publication date

  2024-02-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1016/j.inffus.2024.102307arXiv 2404.04799
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• Advantages and limitations of FSOD algorithms are summarized to surface key challenges, potential research directions, and development trends in data-scarce object detection.

  Confidence 0.93

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review
• A novel taxonomy organizes FSOD algorithms into a comprehensive overview of the field.

  Confidence 0.96

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review

• challenges and limitations

  analysis topic

  The open problems and drawbacks identified across reviewed FSOD approaches.

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review
• data scarcity scenario

  setting

  A training setting where labeled detection data are limited for the target objects.

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review
• development trend

  trend

  The direction of progress in few-shot object detection highlighted by the survey.

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review
• few-shot object detection

  research area

  An object detection setting that adapts detectors to novel classes using only a few labeled examples.

  Aliases: FSOD

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review
• fsod algorithms

  algorithm family

  The family of few-shot object detection methods reviewed and grouped by the taxonomy.

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review
• fsod taxonomy method

  method

  A taxonomy introduced to organize few-shot object detection approaches in the survey.

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) reviewB (s683577b42) reviewAK (4715169a40) review

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