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

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Data labeling for object detection and instance segmentation is costly and time-consuming.
• Few-shot object detection (FSOD) requires extensive labeled data for base classes, despite aiming to detect novel classes with little data.
• Self-supervised learning (SSL) methods learn representations from unlabeled data, beneficial for downstream tasks like object detection.

Purpose of the Study:

• To survey and characterize recent advancements in combining few-shot object detection and self-supervised learning.
• To provide insights into the current state-of-the-art in efficient object detection techniques.
• To identify and discuss promising future research directions in this interdisciplinary area.

Main Methods:

• Review and synthesis of recent academic literature on few-shot and self-supervised object detection.
• Characterization of existing approaches based on their methodologies and performance.
• Analysis of the synergy between FSOD and SSL for representation learning.

Main Results:

• Identified key trends and challenges in integrating FSOD and SSL.
• Highlighted the potential of combined approaches to mitigate data annotation burdens.
• Provided a structured overview of the research landscape.

Conclusions:

• Combining few-shot object detection and self-supervised learning is a highly promising direction for reducing data requirements.
• Further research is needed to fully leverage unlabeled data for efficient and robust object detection.
• The survey offers a roadmap for future investigations in this domain.