Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
This addresses a more challenging and realistic scenario in few-shot learning for AI systems, though it is incremental as it builds on existing few-shot learning frameworks.
The paper tackles the problem of few-shot classification when query classes do not exactly match support classes, by introducing semantic-based few-shot learning that uses higher-level concepts for classification. Results on CIFAR-100 show the method outperforms baselines in this scenario.
Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a single and unique class association. Under these assumptions, these algorithms may not be able to identify the proper class assignment when there is no exact matching between support and query classes. For example, given a few images of lions, bikes, and apples to classify a tiger. However, in a more general setting, we could consider the higher-level concept, the large carnivores, to match the tiger to the lion for semantic classification. Existing studies rarely considered this situation due to the incompatibility of label-based supervision with complex conception relationships. In this work, we advance the few-shot learning towards this more challenging scenario, the semantic-based few-shot learning, and propose a method to address the paradigm by capturing the inner semantic relationships using interactive psychometric learning. The experiment results on the CIFAR-100 dataset show the superiority of our method for the semantic-based few-shot learning compared to the baseline.