What is the role of attention mechanisms in few-shot and zero-shot learning?

Attention mechanisms play a key role in few-shot and zero-shot learning by enabling models to selectively focus on the most relevant parts of input data when training examples are scarce or absent. In few-shot learning, where a model must adapt to new tasks with only a handful of examples, attention helps identify patterns or features shared between the limited training data and new inputs. For zero-shot learning, where models handle tasks they were never explicitly trained on, attention allows them to align inputs with semantic descriptions or attributes of unseen classes. By dynamically weighting the importance of different data elements, attention reduces reliance on large labeled datasets and improves generalization.

A practical example is how transformer-based models use attention for few-shot text classification. Suppose a model is given three examples of a “restaurant review” category. The attention mechanism might focus on words like “service” or “menu” in both the examples and a new query text, amplifying their influence during prediction. For zero-shot tasks, attention can link input features (e.g., an image of an unknown animal) to textual descriptions (e.g., “has stripes and four legs”) stored in the model’s memory. Models like CLIP use cross-modal attention to align image regions with text embeddings of unseen classes, enabling classification without prior training on those classes. This selective focus helps bypass the need for task-specific training data.

Attention also improves efficiency in these scenarios. Instead of processing all input features equally, the mechanism allocates computational resources to critical elements. For instance, in meta-learning frameworks like Prototypical Networks, attention can refine prototype representations by emphasizing discriminative features across support examples. In zero-shot settings, attention over pre-trained knowledge bases (e.g., class hierarchies or attribute lists) lets models reason compositionally—for example, recognizing a “zebra” by combining attention on “horse-like shape” and “striped pattern” from prior knowledge. This adaptability makes attention a flexible tool for scenarios where data is limited or tasks are novel, as it allows models to repurpose existing knowledge without full retraining.