survey

Recent Few-shot Object Detection Algorithms: A Survey with Performance Comparison

Authors:

Jiajia Zhao, and

Shuigeng ZhouAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 4

Article No.: 66, Pages 1 - 36

https://doi.org/10.1145/3593588

Published: 15 June 2023 Publication History

Abstract

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans’ ability of learning to learn and intelligently transfers the learned generic object knowledge from the common heavy-tailed to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.

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Index Terms

Recent Few-shot Object Detection Algorithms: A Survey with Performance Comparison
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
    2. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 4

August 2023

481 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3596215

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 15 June 2023

Online AM: 02 May 2023

Accepted: 12 April 2023

Revised: 29 November 2022

Received: 07 April 2022

Published in TIST Volume 14, Issue 4

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