핵심 개념
Combining few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples.
초록
The content discusses the research advances and challenges in the field of few-shot object detection (FSOD). It first introduces the background and definition of FSOD, emphasizing its potential value in advancing the field of computer vision.
The paper then proposes a novel taxonomy for FSOD methods, classifying them into two broad categories: episode-task-based and single-task-based, based on the concept of transfer learning. It then comprehensively surveys the remarkable FSOD algorithms under this taxonomy, highlighting their motivations and solutions.
Episode-task-based methods follow the principle of meta-learning, dividing the detection task into a series of episode tasks with few-shot samples to assist the model in rapidly adapting to the detection task in the data-scarcity scenario. Single-task-based methods directly transfer the original or fine-tuned parameters of the base model to the novel stage and then fine-tune the few-shot detection task in the novel stage.
The paper discusses the advantages and limitations of these FSOD algorithms, summarizing the challenges, potential research directions, and development trends of object detection in the data scarcity scenario.
통계
"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."
"Fortunately, few-shot learning (FSL) researchers found that even the children who had already learned the knowledge of a dog could learn the concept of a wolf with only a few samples."
"Mainstream FSOD methods borrow ideas from few-shot learning to train the detection network in the data-scarcity scenarios with the help of the prior knowledge learned in the well-annotated base class."
인용구
"Combining FSL with object detection for few-shot object detection (FSOD) is a promising research field, which enables the model to quickly adapt to a few-shot number of annotated objects without weakening the performance."
"All FSOD models that are trained from the base to the novel stage follow the concept of transfer learning."
"Compared with only training Cnovel, G-FSOD is a more comprehensive and balanced detection approach by considering both base and novel classes, addressing the class imbalance, and evaluating the model's performance on a unified test set."