Vision Based Few-Shot Object Detection

Few-shot object detection FSOD addresses the challenge of accurately detecting objects in scenarios where training data is severely limited this paper introduces a novel approach to FSOD leveraging meta-learning techniques to enable models to quickly adapt to new object classes with minimal examples we propose a meta-learning architecture that efficiently exploits the information from a small support set to generalize to unseen classes during inference the effectiveness of the proposed method is demonstrated on benchmark datasets showcasing significant improvements in detection performance under low-data conditions our study contributes to the advancement of object detection in resource-constrained environments showcasing the potential for rapid adaptation and enhanced generalization in scenarios where labeled data is scarce. Additionally our results indicate good performance in various conditions increasing the applicability of FSOD to real-world settings the proposed approach shows robust detection capabilities across images webcam feeds and video streams affirming its versatility and effectiveness.