  In this paper, different techniques of few-shot, zero-shot, and regular
object detection have been investigated. The need for few-shot learning and
zero-shot learning techniques is crucial and arises from the limitations and
challenges in traditional machine learning, deep learning, and computer vision
methods where they require large amounts of data, plus the poor generalization
of those traditional methods.
  Those techniques can give us prominent results by using only a few training
sets reducing the required amounts of data and improving the generalization.
  This survey will highlight the recent papers of the last three years that
introduce the usage of few-shot learning and zero-shot learning techniques in
addressing the challenges mentioned earlier. In this paper we reviewed the
Zero-shot, few-shot and regular object detection methods and categorized them
in an understandable manner. Based on the comparison made within each category.
It been found that the approaches are quite impressive.
  This integrated review of diverse papers on few-shot, zero-shot, and regular
object detection reveals a shared focus on advancing the field through novel
frameworks and techniques. A noteworthy observation is the scarcity of detailed
discussions regarding the difficulties encountered during the development
phase. Contributions include the introduction of innovative models, such as
ZSD-YOLO and GTNet, often showcasing improvements with various metrics such as
mean average precision (mAP),Recall@100 (RE@100), the area under the receiver
operating characteristic curve (AUROC) and precision. These findings underscore
a collective move towards leveraging vision-language models for versatile
applications, with potential areas for future research including a more
thorough exploration of limitations and domain-specific adaptations.
