Task-Covariant Representations for Few-Shot Learning on Remote Sensing Images

In the regression and classification of remotely sensed images through meta-learning, techniques exploit task-invariant information to quickly adapt to new tasks with fewer gradient updates. Despite its usefulness, task-invariant information alone may not effectively capture task-specific knowledge, leading to reduced model performance on new tasks. As a result, the concept of task-covariance has gained significant attention from researchers. We propose task-covariant representations for few-shot Learning on remote sensing images that utilizes capsule networks to effectively represent the covariance relationships among objects. This approach is motivated by the superior ability of capsule networks to capture such relationships. To capture and leverage the covariance relations between tasks, we employ vector capsules and adapt our model parameters based on the newly learned task covariance relations. Our proposed meta-learning algorithm offers a novel approach to effectively address the real task distribution by incorporating both general and specific task information. Based on the experimental results, our proposed meta-learning algorithm shows a significant improvement in both the average accuracy and training efficiency compared to the best model in the experiments. On average, the algorithm increases the accuracy by approximately 4% and improves the training efficiency by approximately 8%.