Few-Shot Object Detection Based on Contrastive Learning

Few-shot object detection is a challenging research topic in the field of computer vision. In scenarios with a limited number of training samples, traditional object detection algorithms often suffer from overfitting, leading to subpar classification accuracy and imprecise localization. To address these challenges, we propose a few-shot object detection algorithm based on contrastive learning, encompassing the design of data strategies and model structures. To mitigate the issues arising from limited data and insufficient intra-class diversity, we introduce data augmentation strategies involving saliency-mixed image enhancement and data resampling. Additionally, to tackle problems such as misclassification of new instances and inaccurate object localization, we design a comprehensive model structure for few-shot object detection based on contrastive learning. Experimental evaluations are conducted on general datasets PASCAL VOC. The results demonstrate the high effectiveness and practicality of the proposed approach in few-shot object detection tasks, underscoring its significant research significance and practical application value.