Few-shot learning in computer vision enables models to recognize new objects or patterns using only a small number of labeled examples. This approach is particularly useful when collecting large datasets is impractical, such as in medical imaging or specialized industrial applications. The core idea is to leverage prior knowledge from related tasks or pre-trained models, then adapt quickly to new tasks with minimal data. Common techniques include meta-learning (training models to learn efficiently across tasks), transfer learning (fine-tuning pre-trained networks), and data augmentation (generating synthetic examples). For instance, a model pre-trained on general object recognition can be adapted to identify rare animal species with just a few images by adjusting only the final classification layer.
A practical example of few-shot learning is in medical imaging, where a model might need to diagnose rare diseases with only a handful of annotated scans. By using a technique like Prototypical Networks, the model creates “prototypes” (average feature representations) for each disease class from the limited examples. New images are classified by comparing their features to these prototypes. Another use case is in robotics, where a robot might need to recognize new tools in a factory setting. A few-shot model could be trained on a base dataset of common tools and then fine-tuned with 5–10 images of each new tool, using data augmentation like rotation or color shifts to simulate variations. This reduces the need for manual labeling while maintaining accuracy.
Developers implementing few-shot learning should consider challenges like overfitting and computational efficiency. Overfitting can be mitigated by using pre-trained feature extractors (e.g., ResNet) and freezing early layers during fine-tuning. Tools like PyTorch’s Torchmeta or TensorFlow’s Few-Shot Learning library provide prebuilt modules for prototyping. For example, a developer could use Torchmeta to load a mini-ImageNet dataset, train a meta-learning model like MAML (Model-Agnostic Meta-Learning), and test its ability to classify unseen objects with five examples per class. Key trade-offs include balancing model complexity (e.g., transformer-based architectures may require more data) versus simpler architectures like Siamese networks, which compare image pairs directly. Prioritizing lightweight models and efficient data augmentation pipelines ensures practical deployment in resource-constrained environments.