META-LEARNING APPROACH FOR FEW-SHOT OBJECT RECOGNITION IN COMPUTER VISION

Few-shot learning (FSL) addresses one of the most fundamental challenges in modern machine learning: the ability to recognize novel object categories from only a small number of labeled training samples. Traditional deep learning models, while achieving remarkable results on standard benchmarks, are heavily dependent on large-scale annotated datasets, limiting their practical deployment in scenarios where data acquisition is expensive, time-consuming, or infeasible. This paper proposes a comprehensive meta-learning based framework for few-shot object recognition in computer vision, leveraging episodic training strategies and deep feature embedding networks to enable rapid adaptation to unseen classes. The proposed system integrates Model-Agnostic Meta-Learning (MAML) with ResNet-based convolutional backbones under a structured N-way K-shot classification protocol. Extensive experiments on benchmark datasets including Mini-ImageNet, Omniglot, and CIFAR-FS demonstrate that the proposed approach achieves 62.1% accuracy in the 1-shot setting and 75.6% in the 5-shot setting on Mini-ImageNet, outperforming CNN baselines by 17.1% and Siamese Network baselines by 6.8%. Ablation studies confirm the individual contributions of backbone depth, distance metric selection, and inner-loop adaptation steps. The results establish the effectiveness of meta-learning for low-data computer vision applications, with promising implications for medical imaging, industrial inspection, and wildlife monitoring.